{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import pandas as pd\n",
    "from glob import glob\n",
    "\n",
    "from typing import List, Tuple, Dict, Any, Union\n",
    "\n",
    "os.chdir('/shared/nas2/xingyao6/projects/Multimodal-Mistral')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "204 data files found\n",
      "204 data files to process (data_DONE exist)\n"
     ]
    }
   ],
   "source": [
    "data_files = glob(\"data/processed/megatron_format/mmistral_*/data.jsonl\")\n",
    "data_files = sorted(data_files)\n",
    "print(f\"{len(data_files)} data files found\")\n",
    "\n",
    "# filter those where data_DONE exist in the same directory\n",
    "data_files = [f for f in data_files if os.path.exists(f.replace(\"data.jsonl\", \"data_DONE\"))]\n",
    "print(f\"{len(data_files)} data files to process (data_DONE exist)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def aggregate_data(data_files, data_key, shard_ids=None):\n",
    "    data = []\n",
    "    for f in data_files:\n",
    "        if data_key in f:\n",
    "            # print(f)\n",
    "            # if shard_ids is not empty, only process files with shard_id in the filename\n",
    "            if shard_ids and f and not re.search(r\"shard_(\\d+)\", f).group(1) in shard_ids:\n",
    "                continue\n",
    "            _cur_df = pd.read_json(f, lines=True)\n",
    "            _cur_df[\"filepath\"] = f\n",
    "            _cur_df[\"n_text_tokens\"] = _cur_df[\"n_tokens\"] - _cur_df[\"n_image_tokens\"]\n",
    "            data.append(_cur_df)\n",
    "    print(f\"{len(data)} data files found with data_key={data_key}\")\n",
    "    data = pd.concat(data)\n",
    "    return data\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def visualize_stats(stats: pd.DataFrame):\n",
    "\n",
    "    display(pd.concat([\n",
    "        stats.describe(),\n",
    "        stats[[\"n_tokens\", \"n_text_tokens\", \"n_image_tokens\", \"n_images\"]].sum().to_frame(\"total\").T\n",
    "    ], axis=0).style.background_gradient(cmap=\"viridis\", axis=1).format(\"{:,.0f}\"))\n",
    "\n",
    "    if stats[\"filepath\"].nunique() > 1:\n",
    "        print(\"Multiple data files found. Showing total token breakdown by file\")\n",
    "        display(stats.groupby(\"filepath\")[[\"n_tokens\", \"n_text_tokens\", \"n_image_tokens\", \"n_images\"]].sum().style.background_gradient(cmap=\"viridis\", axis=1).format(\"{:,.0f}\"))\n",
    "\n",
    "    # plot 3 ecdf subplots for each of the 3 metrics: n_tokens, n_image_tokens, n_images\n",
    "    fig, ax = plt.subplots(1, 4, figsize=(16, 3))\n",
    "    for i, metric in enumerate([\"n_tokens\", \"n_text_tokens\", \"n_image_tokens\", \"n_images\"]):\n",
    "        sns.ecdfplot(stats[metric], ax=ax[i])\n",
    "        # set x-axis to fixed range\n",
    "        ax[i].set_xlim(0, stats[metric].max())\n",
    "        ax[i].set_title(f\"ECDF of {metric}\")\n",
    "    plt.show()\n",
    "\n",
    "# visualize_stats(stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128 data files found with data_key=mmistral_capfusion_shard_\n"
     ]
    },
    {
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       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "    </tr>\n",
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       "      <td id=\"T_fdcb1_row1_col2\" class=\"data row1 col2\" >116</td>\n",
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       "      <td id=\"T_fdcb1_row2_col2\" class=\"data row2 col2\" >10</td>\n",
       "      <td id=\"T_fdcb1_row2_col3\" class=\"data row2 col3\" >454</td>\n",
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       "    <tr>\n",
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       "      <td id=\"T_fdcb1_row3_col1\" class=\"data row3 col1\" >652</td>\n",
       "      <td id=\"T_fdcb1_row3_col2\" class=\"data row3 col2\" >2</td>\n",
       "      <td id=\"T_fdcb1_row3_col3\" class=\"data row3 col3\" >93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdcb1_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
       "      <td id=\"T_fdcb1_row4_col0\" class=\"data row4 col0\" >32,391</td>\n",
       "      <td id=\"T_fdcb1_row4_col1\" class=\"data row4 col1\" >26,479</td>\n",
       "      <td id=\"T_fdcb1_row4_col2\" class=\"data row4 col2\" >109</td>\n",
       "      <td id=\"T_fdcb1_row4_col3\" class=\"data row4 col3\" >5,417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdcb1_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
       "      <td id=\"T_fdcb1_row5_col0\" class=\"data row5 col0\" >32,590</td>\n",
       "      <td id=\"T_fdcb1_row5_col1\" class=\"data row5 col1\" >26,809</td>\n",
       "      <td id=\"T_fdcb1_row5_col2\" class=\"data row5 col2\" >115</td>\n",
       "      <td id=\"T_fdcb1_row5_col3\" class=\"data row5 col3\" >5,711</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_fdcb1_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
       "      <td id=\"T_fdcb1_row6_col0\" class=\"data row6 col0\" >32,696</td>\n",
       "      <td id=\"T_fdcb1_row6_col1\" class=\"data row6 col1\" >27,125</td>\n",
       "      <td id=\"T_fdcb1_row6_col2\" class=\"data row6 col2\" >122</td>\n",
       "      <td id=\"T_fdcb1_row6_col3\" class=\"data row6 col3\" >6,013</td>\n",
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       "      <td id=\"T_fdcb1_row7_col2\" class=\"data row7 col2\" >198</td>\n",
       "      <td id=\"T_fdcb1_row7_col3\" class=\"data row7 col3\" >9,207</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_fdcb1_level0_row8\" class=\"row_heading level0 row8\" >total</th>\n",
       "      <td id=\"T_fdcb1_row8_col0\" class=\"data row8 col0\" >26,571,198,868</td>\n",
       "      <td id=\"T_fdcb1_row8_col1\" class=\"data row8 col1\" >21,894,842,130</td>\n",
       "      <td id=\"T_fdcb1_row8_col2\" class=\"data row8 col2\" >94,427,830</td>\n",
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multiple data files found. Showing total token breakdown by file\n"
     ]
    },
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       "#T_19936_row0_col2, #T_19936_row1_col2, #T_19936_row2_col2, #T_19936_row3_col2, #T_19936_row4_col2, #T_19936_row6_col2, #T_19936_row7_col2, #T_19936_row9_col2, #T_19936_row11_col2, #T_19936_row12_col2, #T_19936_row13_col2, #T_19936_row14_col2, #T_19936_row17_col2, #T_19936_row18_col2, #T_19936_row20_col2, #T_19936_row21_col2, #T_19936_row23_col2, #T_19936_row24_col2, #T_19936_row26_col2, #T_19936_row27_col2, #T_19936_row29_col2, #T_19936_row30_col2, #T_19936_row34_col2, #T_19936_row35_col2, #T_19936_row36_col2, #T_19936_row37_col2, #T_19936_row38_col2, #T_19936_row39_col2, #T_19936_row40_col2, #T_19936_row41_col2, #T_19936_row42_col2, #T_19936_row43_col2, #T_19936_row44_col2, #T_19936_row46_col2, #T_19936_row49_col2, #T_19936_row50_col2, #T_19936_row53_col2, #T_19936_row55_col2, #T_19936_row56_col2, #T_19936_row57_col2, #T_19936_row58_col2, #T_19936_row60_col2, #T_19936_row61_col2, #T_19936_row62_col2, #T_19936_row63_col2, #T_19936_row64_col2, #T_19936_row65_col2, #T_19936_row66_col2, #T_19936_row67_col2, #T_19936_row68_col2, #T_19936_row70_col2, #T_19936_row71_col2, #T_19936_row73_col2, #T_19936_row74_col2, #T_19936_row75_col2, #T_19936_row76_col2, #T_19936_row78_col2, #T_19936_row80_col2, #T_19936_row81_col2, #T_19936_row82_col2, #T_19936_row83_col2, #T_19936_row85_col2, #T_19936_row86_col2, #T_19936_row87_col2, #T_19936_row88_col2, #T_19936_row89_col2, #T_19936_row92_col2, #T_19936_row93_col2, #T_19936_row95_col2, #T_19936_row96_col2, #T_19936_row97_col2, #T_19936_row98_col2, #T_19936_row100_col2, #T_19936_row101_col2, #T_19936_row102_col2, #T_19936_row104_col2, #T_19936_row105_col2, #T_19936_row106_col2, #T_19936_row108_col2, #T_19936_row109_col2, #T_19936_row110_col2, #T_19936_row111_col2, #T_19936_row112_col2, #T_19936_row113_col2, #T_19936_row115_col2, #T_19936_row117_col2, #T_19936_row118_col2, #T_19936_row119_col2, #T_19936_row120_col2, #T_19936_row121_col2, #T_19936_row122_col2, #T_19936_row123_col2, #T_19936_row124_col2, #T_19936_row126_col2, #T_19936_row127_col2 {\n",
       "  background-color: #89d548;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_19936_row0_col3, #T_19936_row1_col3, #T_19936_row2_col3, #T_19936_row3_col3, #T_19936_row4_col3, #T_19936_row5_col3, #T_19936_row6_col3, #T_19936_row7_col3, #T_19936_row8_col3, #T_19936_row9_col3, #T_19936_row10_col3, #T_19936_row11_col3, #T_19936_row12_col3, #T_19936_row13_col3, #T_19936_row14_col3, #T_19936_row15_col3, #T_19936_row16_col3, #T_19936_row17_col3, #T_19936_row18_col3, #T_19936_row19_col3, #T_19936_row20_col3, #T_19936_row21_col3, #T_19936_row22_col3, #T_19936_row23_col3, #T_19936_row24_col3, #T_19936_row25_col3, #T_19936_row26_col3, #T_19936_row27_col3, #T_19936_row28_col3, #T_19936_row29_col3, #T_19936_row30_col3, #T_19936_row31_col3, #T_19936_row32_col3, #T_19936_row33_col3, #T_19936_row34_col3, #T_19936_row35_col3, #T_19936_row36_col3, #T_19936_row37_col3, #T_19936_row38_col3, #T_19936_row39_col3, #T_19936_row40_col3, #T_19936_row41_col3, #T_19936_row42_col3, #T_19936_row43_col3, #T_19936_row44_col3, #T_19936_row45_col3, #T_19936_row46_col3, #T_19936_row47_col3, #T_19936_row48_col3, #T_19936_row49_col3, #T_19936_row50_col3, #T_19936_row51_col3, #T_19936_row52_col3, #T_19936_row53_col3, #T_19936_row54_col3, #T_19936_row55_col3, #T_19936_row56_col3, #T_19936_row57_col3, #T_19936_row58_col3, #T_19936_row59_col3, #T_19936_row60_col3, #T_19936_row61_col3, #T_19936_row62_col3, #T_19936_row63_col3, #T_19936_row64_col3, #T_19936_row65_col3, #T_19936_row66_col3, #T_19936_row67_col3, #T_19936_row68_col3, #T_19936_row69_col3, #T_19936_row70_col3, #T_19936_row71_col3, #T_19936_row72_col3, #T_19936_row73_col3, #T_19936_row74_col3, #T_19936_row75_col3, #T_19936_row76_col3, #T_19936_row77_col3, #T_19936_row78_col3, #T_19936_row79_col3, #T_19936_row80_col3, #T_19936_row81_col3, #T_19936_row82_col3, #T_19936_row83_col3, #T_19936_row84_col3, #T_19936_row85_col3, #T_19936_row86_col3, #T_19936_row87_col3, #T_19936_row88_col3, #T_19936_row89_col3, #T_19936_row90_col3, #T_19936_row91_col3, #T_19936_row92_col3, #T_19936_row93_col3, #T_19936_row94_col3, #T_19936_row95_col3, #T_19936_row96_col3, #T_19936_row97_col3, #T_19936_row98_col3, #T_19936_row99_col3, #T_19936_row100_col3, #T_19936_row101_col3, #T_19936_row102_col3, #T_19936_row103_col3, #T_19936_row104_col3, #T_19936_row105_col3, #T_19936_row106_col3, #T_19936_row107_col3, #T_19936_row108_col3, #T_19936_row109_col3, #T_19936_row110_col3, #T_19936_row111_col3, #T_19936_row112_col3, #T_19936_row113_col3, #T_19936_row114_col3, #T_19936_row115_col3, #T_19936_row116_col3, #T_19936_row117_col3, #T_19936_row118_col3, #T_19936_row119_col3, #T_19936_row120_col3, #T_19936_row121_col3, #T_19936_row122_col3, #T_19936_row123_col3, #T_19936_row124_col3, #T_19936_row125_col3, #T_19936_row126_col3, #T_19936_row127_col3 {\n",
       "  background-color: #440154;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_19936_row5_col1, #T_19936_row8_col1, #T_19936_row10_col1, #T_19936_row19_col1, #T_19936_row22_col1, #T_19936_row32_col1, #T_19936_row33_col1, #T_19936_row47_col1, #T_19936_row54_col1, #T_19936_row59_col1, #T_19936_row72_col1, #T_19936_row77_col1, #T_19936_row84_col1, #T_19936_row90_col1, #T_19936_row99_col1, #T_19936_row103_col1, #T_19936_row107_col1, #T_19936_row114_col1 {\n",
       "  background-color: #443a83;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_19936_row5_col2, #T_19936_row8_col2, #T_19936_row10_col2, #T_19936_row15_col2, #T_19936_row16_col2, #T_19936_row19_col2, #T_19936_row22_col2, #T_19936_row25_col2, #T_19936_row28_col2, #T_19936_row31_col2, #T_19936_row32_col2, #T_19936_row33_col2, #T_19936_row45_col2, #T_19936_row47_col2, #T_19936_row48_col2, #T_19936_row51_col2, #T_19936_row52_col2, #T_19936_row54_col2, #T_19936_row59_col2, #T_19936_row69_col2, #T_19936_row72_col2, #T_19936_row77_col2, #T_19936_row79_col2, #T_19936_row84_col2, #T_19936_row90_col2, #T_19936_row91_col2, #T_19936_row94_col2, #T_19936_row99_col2, #T_19936_row103_col2, #T_19936_row107_col2, #T_19936_row114_col2, #T_19936_row116_col2, #T_19936_row125_col2 {\n",
       "  background-color: #8bd646;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_19936\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_19936_level0_col0\" class=\"col_heading level0 col0\" >n_tokens</th>\n",
       "      <th id=\"T_19936_level0_col1\" class=\"col_heading level0 col1\" >n_text_tokens</th>\n",
       "      <th id=\"T_19936_level0_col2\" class=\"col_heading level0 col2\" >n_image_tokens</th>\n",
       "      <th id=\"T_19936_level0_col3\" class=\"col_heading level0 col3\" >n_images</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >filepath</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row0\" class=\"row_heading level0 row0\" >data/processed/megatron_format/mmistral_capfusion_shard_000_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row0_col0\" class=\"data row0 col0\" >207,815,782</td>\n",
       "      <td id=\"T_19936_row0_col1\" class=\"data row0 col1\" >36,655,853</td>\n",
       "      <td id=\"T_19936_row0_col2\" class=\"data row0 col2\" >171,159,929</td>\n",
       "      <td id=\"T_19936_row0_col3\" class=\"data row0 col3\" >740,163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row1\" class=\"row_heading level0 row1\" >data/processed/megatron_format/mmistral_capfusion_shard_001_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row1_col0\" class=\"data row1 col0\" >206,774,364</td>\n",
       "      <td id=\"T_19936_row1_col1\" class=\"data row1 col1\" >36,571,627</td>\n",
       "      <td id=\"T_19936_row1_col2\" class=\"data row1 col2\" >170,202,737</td>\n",
       "      <td id=\"T_19936_row1_col3\" class=\"data row1 col3\" >739,583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row2\" class=\"row_heading level0 row2\" >data/processed/megatron_format/mmistral_capfusion_shard_002_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row2_col0\" class=\"data row2 col0\" >208,615,428</td>\n",
       "      <td id=\"T_19936_row2_col1\" class=\"data row2 col1\" >36,747,426</td>\n",
       "      <td id=\"T_19936_row2_col2\" class=\"data row2 col2\" >171,868,002</td>\n",
       "      <td id=\"T_19936_row2_col3\" class=\"data row2 col3\" >740,771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row3\" class=\"row_heading level0 row3\" >data/processed/megatron_format/mmistral_capfusion_shard_003_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row3_col0\" class=\"data row3 col0\" >206,227,749</td>\n",
       "      <td id=\"T_19936_row3_col1\" class=\"data row3 col1\" >36,568,754</td>\n",
       "      <td id=\"T_19936_row3_col2\" class=\"data row3 col2\" >169,658,995</td>\n",
       "      <td id=\"T_19936_row3_col3\" class=\"data row3 col3\" >739,404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row4\" class=\"row_heading level0 row4\" >data/processed/megatron_format/mmistral_capfusion_shard_004_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row4_col0\" class=\"data row4 col0\" >208,920,072</td>\n",
       "      <td id=\"T_19936_row4_col1\" class=\"data row4 col1\" >36,745,513</td>\n",
       "      <td id=\"T_19936_row4_col2\" class=\"data row4 col2\" >172,174,559</td>\n",
       "      <td id=\"T_19936_row4_col3\" class=\"data row4 col3\" >741,079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row5\" class=\"row_heading level0 row5\" >data/processed/megatron_format/mmistral_capfusion_shard_005_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row5_col0\" class=\"data row5 col0\" >210,832,912</td>\n",
       "      <td id=\"T_19936_row5_col1\" class=\"data row5 col1\" >36,765,302</td>\n",
       "      <td id=\"T_19936_row5_col2\" class=\"data row5 col2\" >174,067,610</td>\n",
       "      <td id=\"T_19936_row5_col3\" class=\"data row5 col3\" >741,748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row6\" class=\"row_heading level0 row6\" >data/processed/megatron_format/mmistral_capfusion_shard_006_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row6_col0\" class=\"data row6 col0\" >207,911,194</td>\n",
       "      <td id=\"T_19936_row6_col1\" class=\"data row6 col1\" >36,609,107</td>\n",
       "      <td id=\"T_19936_row6_col2\" class=\"data row6 col2\" >171,302,087</td>\n",
       "      <td id=\"T_19936_row6_col3\" class=\"data row6 col3\" >739,923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row7\" class=\"row_heading level0 row7\" >data/processed/megatron_format/mmistral_capfusion_shard_007_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row7_col0\" class=\"data row7 col0\" >208,287,357</td>\n",
       "      <td id=\"T_19936_row7_col1\" class=\"data row7 col1\" >36,663,998</td>\n",
       "      <td id=\"T_19936_row7_col2\" class=\"data row7 col2\" >171,623,359</td>\n",
       "      <td id=\"T_19936_row7_col3\" class=\"data row7 col3\" >740,591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row8\" class=\"row_heading level0 row8\" >data/processed/megatron_format/mmistral_capfusion_shard_008_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row8_col0\" class=\"data row8 col0\" >210,584,435</td>\n",
       "      <td id=\"T_19936_row8_col1\" class=\"data row8 col1\" >36,796,178</td>\n",
       "      <td id=\"T_19936_row8_col2\" class=\"data row8 col2\" >173,788,257</td>\n",
       "      <td id=\"T_19936_row8_col3\" class=\"data row8 col3\" >741,689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row9\" class=\"row_heading level0 row9\" >data/processed/megatron_format/mmistral_capfusion_shard_009_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row9_col0\" class=\"data row9 col0\" >206,667,352</td>\n",
       "      <td id=\"T_19936_row9_col1\" class=\"data row9 col1\" >36,681,217</td>\n",
       "      <td id=\"T_19936_row9_col2\" class=\"data row9 col2\" >169,986,135</td>\n",
       "      <td id=\"T_19936_row9_col3\" class=\"data row9 col3\" >740,539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row10\" class=\"row_heading level0 row10\" >data/processed/megatron_format/mmistral_capfusion_shard_010_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row10_col0\" class=\"data row10 col0\" >210,939,494</td>\n",
       "      <td id=\"T_19936_row10_col1\" class=\"data row10 col1\" >36,707,229</td>\n",
       "      <td id=\"T_19936_row10_col2\" class=\"data row10 col2\" >174,232,265</td>\n",
       "      <td id=\"T_19936_row10_col3\" class=\"data row10 col3\" >740,782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row11\" class=\"row_heading level0 row11\" >data/processed/megatron_format/mmistral_capfusion_shard_011_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row11_col0\" class=\"data row11 col0\" >204,985,995</td>\n",
       "      <td id=\"T_19936_row11_col1\" class=\"data row11 col1\" >36,482,893</td>\n",
       "      <td id=\"T_19936_row11_col2\" class=\"data row11 col2\" >168,503,102</td>\n",
       "      <td id=\"T_19936_row11_col3\" class=\"data row11 col3\" >738,883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row12\" class=\"row_heading level0 row12\" >data/processed/megatron_format/mmistral_capfusion_shard_012_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row12_col0\" class=\"data row12 col0\" >209,796,060</td>\n",
       "      <td id=\"T_19936_row12_col1\" class=\"data row12 col1\" >36,799,784</td>\n",
       "      <td id=\"T_19936_row12_col2\" class=\"data row12 col2\" >172,996,276</td>\n",
       "      <td id=\"T_19936_row12_col3\" class=\"data row12 col3\" >741,327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row13\" class=\"row_heading level0 row13\" >data/processed/megatron_format/mmistral_capfusion_shard_013_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row13_col0\" class=\"data row13 col0\" >208,169,220</td>\n",
       "      <td id=\"T_19936_row13_col1\" class=\"data row13 col1\" >36,571,537</td>\n",
       "      <td id=\"T_19936_row13_col2\" class=\"data row13 col2\" >171,597,683</td>\n",
       "      <td id=\"T_19936_row13_col3\" class=\"data row13 col3\" >738,966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row14\" class=\"row_heading level0 row14\" >data/processed/megatron_format/mmistral_capfusion_shard_014_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row14_col0\" class=\"data row14 col0\" >207,921,445</td>\n",
       "      <td id=\"T_19936_row14_col1\" class=\"data row14 col1\" >36,632,592</td>\n",
       "      <td id=\"T_19936_row14_col2\" class=\"data row14 col2\" >171,288,853</td>\n",
       "      <td id=\"T_19936_row14_col3\" class=\"data row14 col3\" >739,122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row15\" class=\"row_heading level0 row15\" >data/processed/megatron_format/mmistral_capfusion_shard_015_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row15_col0\" class=\"data row15 col0\" >209,603,298</td>\n",
       "      <td id=\"T_19936_row15_col1\" class=\"data row15 col1\" >36,662,340</td>\n",
       "      <td id=\"T_19936_row15_col2\" class=\"data row15 col2\" >172,940,958</td>\n",
       "      <td id=\"T_19936_row15_col3\" class=\"data row15 col3\" >739,598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row16\" class=\"row_heading level0 row16\" >data/processed/megatron_format/mmistral_capfusion_shard_016_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row16_col0\" class=\"data row16 col0\" >209,858,056</td>\n",
       "      <td id=\"T_19936_row16_col1\" class=\"data row16 col1\" >36,730,258</td>\n",
       "      <td id=\"T_19936_row16_col2\" class=\"data row16 col2\" >173,127,798</td>\n",
       "      <td id=\"T_19936_row16_col3\" class=\"data row16 col3\" >740,990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row17\" class=\"row_heading level0 row17\" >data/processed/megatron_format/mmistral_capfusion_shard_017_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row17_col0\" class=\"data row17 col0\" >208,641,487</td>\n",
       "      <td id=\"T_19936_row17_col1\" class=\"data row17 col1\" >36,694,177</td>\n",
       "      <td id=\"T_19936_row17_col2\" class=\"data row17 col2\" >171,947,310</td>\n",
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       "      <th id=\"T_19936_level0_row118\" class=\"row_heading level0 row118\" >data/processed/megatron_format/mmistral_capfusion_shard_118_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row118_col0\" class=\"data row118 col0\" >208,578,400</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_19936_level0_row119\" class=\"row_heading level0 row119\" >data/processed/megatron_format/mmistral_capfusion_shard_119_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row119_col0\" class=\"data row119 col0\" >208,281,868</td>\n",
       "      <td id=\"T_19936_row119_col1\" class=\"data row119 col1\" >36,623,980</td>\n",
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       "      <td id=\"T_19936_row119_col3\" class=\"data row119 col3\" >740,319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row120\" class=\"row_heading level0 row120\" >data/processed/megatron_format/mmistral_capfusion_shard_120_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row120_col0\" class=\"data row120 col0\" >207,986,662</td>\n",
       "      <td id=\"T_19936_row120_col1\" class=\"data row120 col1\" >36,651,328</td>\n",
       "      <td id=\"T_19936_row120_col2\" class=\"data row120 col2\" >171,335,334</td>\n",
       "      <td id=\"T_19936_row120_col3\" class=\"data row120 col3\" >739,420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row121\" class=\"row_heading level0 row121\" >data/processed/megatron_format/mmistral_capfusion_shard_121_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row121_col0\" class=\"data row121 col0\" >207,545,990</td>\n",
       "      <td id=\"T_19936_row121_col1\" class=\"data row121 col1\" >36,698,942</td>\n",
       "      <td id=\"T_19936_row121_col2\" class=\"data row121 col2\" >170,847,048</td>\n",
       "      <td id=\"T_19936_row121_col3\" class=\"data row121 col3\" >740,996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row122\" class=\"row_heading level0 row122\" >data/processed/megatron_format/mmistral_capfusion_shard_122_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row122_col0\" class=\"data row122 col0\" >209,692,726</td>\n",
       "      <td id=\"T_19936_row122_col1\" class=\"data row122 col1\" >36,752,389</td>\n",
       "      <td id=\"T_19936_row122_col2\" class=\"data row122 col2\" >172,940,337</td>\n",
       "      <td id=\"T_19936_row122_col3\" class=\"data row122 col3\" >741,685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row123\" class=\"row_heading level0 row123\" >data/processed/megatron_format/mmistral_capfusion_shard_123_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row123_col0\" class=\"data row123 col0\" >207,643,850</td>\n",
       "      <td id=\"T_19936_row123_col1\" class=\"data row123 col1\" >36,646,184</td>\n",
       "      <td id=\"T_19936_row123_col2\" class=\"data row123 col2\" >170,997,666</td>\n",
       "      <td id=\"T_19936_row123_col3\" class=\"data row123 col3\" >739,745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row124\" class=\"row_heading level0 row124\" >data/processed/megatron_format/mmistral_capfusion_shard_124_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row124_col0\" class=\"data row124 col0\" >207,881,666</td>\n",
       "      <td id=\"T_19936_row124_col1\" class=\"data row124 col1\" >36,659,904</td>\n",
       "      <td id=\"T_19936_row124_col2\" class=\"data row124 col2\" >171,221,762</td>\n",
       "      <td id=\"T_19936_row124_col3\" class=\"data row124 col3\" >739,638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row125\" class=\"row_heading level0 row125\" >data/processed/megatron_format/mmistral_capfusion_shard_125_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row125_col0\" class=\"data row125 col0\" >209,435,905</td>\n",
       "      <td id=\"T_19936_row125_col1\" class=\"data row125 col1\" >36,635,688</td>\n",
       "      <td id=\"T_19936_row125_col2\" class=\"data row125 col2\" >172,800,217</td>\n",
       "      <td id=\"T_19936_row125_col3\" class=\"data row125 col3\" >739,820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row126\" class=\"row_heading level0 row126\" >data/processed/megatron_format/mmistral_capfusion_shard_126_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row126_col0\" class=\"data row126 col0\" >207,544,906</td>\n",
       "      <td id=\"T_19936_row126_col1\" class=\"data row126 col1\" >36,700,197</td>\n",
       "      <td id=\"T_19936_row126_col2\" class=\"data row126 col2\" >170,844,709</td>\n",
       "      <td id=\"T_19936_row126_col3\" class=\"data row126 col3\" >741,297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_19936_level0_row127\" class=\"row_heading level0 row127\" >data/processed/megatron_format/mmistral_capfusion_shard_127_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_19936_row127_col0\" class=\"data row127 col0\" >125,779,540</td>\n",
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       "      <td id=\"T_19936_row127_col2\" class=\"data row127 col2\" >103,549,702</td>\n",
       "      <td id=\"T_19936_row127_col3\" class=\"data row127 col3\" >448,793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f8cdb37bf40>"
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     "output_type": "display_data"
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",
      "text/plain": [
       "<Figure size 1600x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "capfusion = aggregate_data(data_files, \"mmistral_capfusion_shard_\")\n",
    "visualize_stats(capfusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 data files found with data_key=mmistral_detailed_captions_shard_\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_10739_row0_col0, #T_10739_row0_col1, #T_10739_row0_col2, #T_10739_row0_col3, #T_10739_row1_col2, #T_10739_row2_col2, #T_10739_row3_col2, #T_10739_row4_col2, #T_10739_row5_col2, #T_10739_row6_col2, #T_10739_row7_col2, #T_10739_row8_col2 {\n",
       "  background-color: #440154;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_10739_row1_col0, #T_10739_row2_col1, #T_10739_row3_col0, #T_10739_row4_col0, #T_10739_row5_col0, #T_10739_row6_col0, #T_10739_row7_col0, #T_10739_row8_col0 {\n",
       "  background-color: #fde725;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_10739_row1_col1, #T_10739_row8_col1 {\n",
       "  background-color: #6ece58;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_10739_row1_col3, #T_10739_row5_col3, #T_10739_row8_col3 {\n",
       "  background-color: #3e4989;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_10739_row2_col0 {\n",
       "  background-color: #37b878;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_10739_row2_col3 {\n",
       "  background-color: #a0da39;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_10739_row3_col1 {\n",
       "  background-color: #7ad151;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_10739_row3_col3 {\n",
       "  background-color: #414487;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_10739_row4_col1, #T_10739_row5_col1 {\n",
       "  background-color: #6ccd5a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_10739_row4_col3 {\n",
       "  background-color: #3f4788;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_10739_row6_col1 {\n",
       "  background-color: #70cf57;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_10739_row6_col3 {\n",
       "  background-color: #3e4a89;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_10739_row7_col1 {\n",
       "  background-color: #86d549;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_10739_row7_col3 {\n",
       "  background-color: #39558c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_10739\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_10739_level0_col0\" class=\"col_heading level0 col0\" >n_tokens</th>\n",
       "      <th id=\"T_10739_level0_col1\" class=\"col_heading level0 col1\" >n_image_tokens</th>\n",
       "      <th id=\"T_10739_level0_col2\" class=\"col_heading level0 col2\" >n_images</th>\n",
       "      <th id=\"T_10739_level0_col3\" class=\"col_heading level0 col3\" >n_text_tokens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_10739_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
       "      <td id=\"T_10739_row0_col0\" class=\"data row0 col0\" >6,225</td>\n",
       "      <td id=\"T_10739_row0_col1\" class=\"data row0 col1\" >6,225</td>\n",
       "      <td id=\"T_10739_row0_col2\" class=\"data row0 col2\" >6,225</td>\n",
       "      <td id=\"T_10739_row0_col3\" class=\"data row0 col3\" >6,225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_10739_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
       "      <td id=\"T_10739_row1_col0\" class=\"data row1 col0\" >32,452</td>\n",
       "      <td id=\"T_10739_row1_col1\" class=\"data row1 col1\" >25,258</td>\n",
       "      <td id=\"T_10739_row1_col2\" class=\"data row1 col2\" >59</td>\n",
       "      <td id=\"T_10739_row1_col3\" class=\"data row1 col3\" >7,195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_10739_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
       "      <td id=\"T_10739_row2_col0\" class=\"data row2 col0\" >256</td>\n",
       "      <td id=\"T_10739_row2_col1\" class=\"data row2 col1\" >382</td>\n",
       "      <td id=\"T_10739_row2_col2\" class=\"data row2 col2\" >3</td>\n",
       "      <td id=\"T_10739_row2_col3\" class=\"data row2 col3\" >328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_10739_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
       "      <td id=\"T_10739_row3_col0\" class=\"data row3 col0\" >22,119</td>\n",
       "      <td id=\"T_10739_row3_col1\" class=\"data row3 col1\" >17,639</td>\n",
       "      <td id=\"T_10739_row3_col2\" class=\"data row3 col2\" >35</td>\n",
       "      <td id=\"T_10739_row3_col3\" class=\"data row3 col3\" >4,480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_10739_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
       "      <td id=\"T_10739_row4_col0\" class=\"data row4 col0\" >32,325</td>\n",
       "      <td id=\"T_10739_row4_col1\" class=\"data row4 col1\" >25,020</td>\n",
       "      <td id=\"T_10739_row4_col2\" class=\"data row4 col2\" >57</td>\n",
       "      <td id=\"T_10739_row4_col3\" class=\"data row4 col3\" >6,974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_10739_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
       "      <td id=\"T_10739_row5_col0\" class=\"data row5 col0\" >32,492</td>\n",
       "      <td id=\"T_10739_row5_col1\" class=\"data row5 col1\" >25,260</td>\n",
       "      <td id=\"T_10739_row5_col2\" class=\"data row5 col2\" >59</td>\n",
       "      <td id=\"T_10739_row5_col3\" class=\"data row5 col3\" >7,195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_10739_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
       "      <td id=\"T_10739_row6_col0\" class=\"data row6 col0\" >32,627</td>\n",
       "      <td id=\"T_10739_row6_col1\" class=\"data row6 col1\" >25,510</td>\n",
       "      <td id=\"T_10739_row6_col2\" class=\"data row6 col2\" >61</td>\n",
       "      <td id=\"T_10739_row6_col3\" class=\"data row6 col3\" >7,417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_10739_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
       "      <td id=\"T_10739_row7_col0\" class=\"data row7 col0\" >32,768</td>\n",
       "      <td id=\"T_10739_row7_col1\" class=\"data row7 col1\" >26,833</td>\n",
       "      <td id=\"T_10739_row7_col2\" class=\"data row7 col2\" >70</td>\n",
       "      <td id=\"T_10739_row7_col3\" class=\"data row7 col3\" >8,687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_10739_level0_row8\" class=\"row_heading level0 row8\" >total</th>\n",
       "      <td id=\"T_10739_row8_col0\" class=\"data row8 col0\" >202,016,770</td>\n",
       "      <td id=\"T_10739_row8_col1\" class=\"data row8 col1\" >157,228,570</td>\n",
       "      <td id=\"T_10739_row8_col2\" class=\"data row8 col2\" >368,767</td>\n",
       "      <td id=\"T_10739_row8_col3\" class=\"data row8 col3\" >44,788,200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f8cda3ceda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "detailed_caption = aggregate_data(data_files, \"mmistral_detailed_captions_shard_\")\n",
    "visualize_stats(detailed_caption)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 data files found with data_key=mmistral_cc3m_full_shard_\n"
     ]
    },
    {
     "data": {
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       "    <tr>\n",
       "      <th id=\"T_5847b_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
       "      <td id=\"T_5847b_row0_col0\" class=\"data row0 col0\" >32,760</td>\n",
       "      <td id=\"T_5847b_row0_col1\" class=\"data row0 col1\" >32,760</td>\n",
       "      <td id=\"T_5847b_row0_col2\" class=\"data row0 col2\" >32,760</td>\n",
       "      <td id=\"T_5847b_row0_col3\" class=\"data row0 col3\" >32,760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5847b_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
       "      <td id=\"T_5847b_row1_col0\" class=\"data row1 col0\" >32,493</td>\n",
       "      <td id=\"T_5847b_row1_col1\" class=\"data row1 col1\" >30,170</td>\n",
       "      <td id=\"T_5847b_row1_col2\" class=\"data row1 col2\" >71</td>\n",
       "      <td id=\"T_5847b_row1_col3\" class=\"data row1 col3\" >2,323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5847b_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
       "      <td id=\"T_5847b_row2_col0\" class=\"data row2 col0\" >336</td>\n",
       "      <td id=\"T_5847b_row2_col1\" class=\"data row2 col1\" >326</td>\n",
       "      <td id=\"T_5847b_row2_col2\" class=\"data row2 col2\" >4</td>\n",
       "      <td id=\"T_5847b_row2_col3\" class=\"data row2 col3\" >96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5847b_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
       "      <td id=\"T_5847b_row3_col0\" class=\"data row3 col0\" >4,956</td>\n",
       "      <td id=\"T_5847b_row3_col1\" class=\"data row3 col1\" >4,556</td>\n",
       "      <td id=\"T_5847b_row3_col2\" class=\"data row3 col2\" >12</td>\n",
       "      <td id=\"T_5847b_row3_col3\" class=\"data row3 col3\" >400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5847b_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
       "      <td id=\"T_5847b_row4_col0\" class=\"data row4 col0\" >32,382</td>\n",
       "      <td id=\"T_5847b_row4_col1\" class=\"data row4 col1\" >30,049</td>\n",
       "      <td id=\"T_5847b_row4_col2\" class=\"data row4 col2\" >69</td>\n",
       "      <td id=\"T_5847b_row4_col3\" class=\"data row4 col3\" >2,260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5847b_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
       "      <td id=\"T_5847b_row5_col0\" class=\"data row5 col0\" >32,539</td>\n",
       "      <td id=\"T_5847b_row5_col1\" class=\"data row5 col1\" >30,204</td>\n",
       "      <td id=\"T_5847b_row5_col2\" class=\"data row5 col2\" >71</td>\n",
       "      <td id=\"T_5847b_row5_col3\" class=\"data row5 col3\" >2,322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5847b_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
       "      <td id=\"T_5847b_row6_col0\" class=\"data row6 col0\" >32,654</td>\n",
       "      <td id=\"T_5847b_row6_col1\" class=\"data row6 col1\" >30,327</td>\n",
       "      <td id=\"T_5847b_row6_col2\" class=\"data row6 col2\" >74</td>\n",
       "      <td id=\"T_5847b_row6_col3\" class=\"data row6 col3\" >2,386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5847b_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
       "      <td id=\"T_5847b_row7_col0\" class=\"data row7 col0\" >32,768</td>\n",
       "      <td id=\"T_5847b_row7_col1\" class=\"data row7 col1\" >30,711</td>\n",
       "      <td id=\"T_5847b_row7_col2\" class=\"data row7 col2\" >86</td>\n",
       "      <td id=\"T_5847b_row7_col3\" class=\"data row7 col3\" >2,698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5847b_level0_row8\" class=\"row_heading level0 row8\" >total</th>\n",
       "      <td id=\"T_5847b_row8_col0\" class=\"data row8 col0\" >1,064,477,314</td>\n",
       "      <td id=\"T_5847b_row8_col1\" class=\"data row8 col1\" >988,385,167</td>\n",
       "      <td id=\"T_5847b_row8_col2\" class=\"data row8 col2\" >2,331,439</td>\n",
       "      <td id=\"T_5847b_row8_col3\" class=\"data row8 col3\" >76,092,147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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       "<pandas.io.formats.style.Styler at 0x7f8c4ace20e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multiple data files found. Showing total token breakdown by file\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_24c9c_row0_col0, #T_24c9c_row1_col0, #T_24c9c_row2_col0, #T_24c9c_row3_col0, #T_24c9c_row4_col0, #T_24c9c_row5_col0, #T_24c9c_row6_col0, #T_24c9c_row7_col0, #T_24c9c_row8_col0 {\n",
       "  background-color: #fde725;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_24c9c_row0_col1, #T_24c9c_row1_col1, #T_24c9c_row2_col1, #T_24c9c_row3_col1, #T_24c9c_row4_col1, #T_24c9c_row5_col1, #T_24c9c_row6_col1, #T_24c9c_row7_col1, #T_24c9c_row8_col1 {\n",
       "  background-color: #481a6c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_24c9c_row0_col2, #T_24c9c_row1_col2, #T_24c9c_row2_col2, #T_24c9c_row3_col2, #T_24c9c_row4_col2, #T_24c9c_row5_col2, #T_24c9c_row6_col2, #T_24c9c_row7_col2, #T_24c9c_row8_col2 {\n",
       "  background-color: #d0e11c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_24c9c_row0_col3, #T_24c9c_row1_col3, #T_24c9c_row2_col3, #T_24c9c_row3_col3, #T_24c9c_row4_col3, #T_24c9c_row5_col3, #T_24c9c_row6_col3, #T_24c9c_row7_col3, #T_24c9c_row8_col3 {\n",
       "  background-color: #440154;\n",
       "  color: #f1f1f1;\n",
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       "</style>\n",
       "<table id=\"T_24c9c\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_24c9c_level0_col0\" class=\"col_heading level0 col0\" >n_tokens</th>\n",
       "      <th id=\"T_24c9c_level0_col1\" class=\"col_heading level0 col1\" >n_text_tokens</th>\n",
       "      <th id=\"T_24c9c_level0_col2\" class=\"col_heading level0 col2\" >n_image_tokens</th>\n",
       "      <th id=\"T_24c9c_level0_col3\" class=\"col_heading level0 col3\" >n_images</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >filepath</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
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       "      <th class=\"blank col3\" >&nbsp;</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_24c9c_level0_row0\" class=\"row_heading level0 row0\" >data/processed/megatron_format/mmistral_cc3m_full_shard_000_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_24c9c_row0_col0\" class=\"data row0 col0\" >118,693,135</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24c9c_level0_row1\" class=\"row_heading level0 row1\" >data/processed/megatron_format/mmistral_cc3m_full_shard_001_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_24c9c_row1_col0\" class=\"data row1 col0\" >118,554,998</td>\n",
       "      <td id=\"T_24c9c_row1_col1\" class=\"data row1 col1\" >8,480,315</td>\n",
       "      <td id=\"T_24c9c_row1_col2\" class=\"data row1 col2\" >110,074,683</td>\n",
       "      <td id=\"T_24c9c_row1_col3\" class=\"data row1 col3\" >259,625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24c9c_level0_row2\" class=\"row_heading level0 row2\" >data/processed/megatron_format/mmistral_cc3m_full_shard_002_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_24c9c_row2_col0\" class=\"data row2 col0\" >118,371,797</td>\n",
       "      <td id=\"T_24c9c_row2_col1\" class=\"data row2 col1\" >8,455,952</td>\n",
       "      <td id=\"T_24c9c_row2_col2\" class=\"data row2 col2\" >109,915,845</td>\n",
       "      <td id=\"T_24c9c_row2_col3\" class=\"data row2 col3\" >259,139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24c9c_level0_row3\" class=\"row_heading level0 row3\" >data/processed/megatron_format/mmistral_cc3m_full_shard_003_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_24c9c_row3_col0\" class=\"data row3 col0\" >118,648,756</td>\n",
       "      <td id=\"T_24c9c_row3_col1\" class=\"data row3 col1\" >8,479,185</td>\n",
       "      <td id=\"T_24c9c_row3_col2\" class=\"data row3 col2\" >110,169,571</td>\n",
       "      <td id=\"T_24c9c_row3_col3\" class=\"data row3 col3\" >259,824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24c9c_level0_row4\" class=\"row_heading level0 row4\" >data/processed/megatron_format/mmistral_cc3m_full_shard_004_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_24c9c_row4_col0\" class=\"data row4 col0\" >118,855,962</td>\n",
       "      <td id=\"T_24c9c_row4_col1\" class=\"data row4 col1\" >8,500,136</td>\n",
       "      <td id=\"T_24c9c_row4_col2\" class=\"data row4 col2\" >110,355,826</td>\n",
       "      <td id=\"T_24c9c_row4_col3\" class=\"data row4 col3\" >260,310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24c9c_level0_row5\" class=\"row_heading level0 row5\" >data/processed/megatron_format/mmistral_cc3m_full_shard_005_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_24c9c_row5_col0\" class=\"data row5 col0\" >118,647,069</td>\n",
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       "      <td id=\"T_24c9c_row5_col2\" class=\"data row5 col2\" >110,168,768</td>\n",
       "      <td id=\"T_24c9c_row5_col3\" class=\"data row5 col3\" >259,771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24c9c_level0_row6\" class=\"row_heading level0 row6\" >data/processed/megatron_format/mmistral_cc3m_full_shard_006_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_24c9c_row6_col0\" class=\"data row6 col0\" >118,588,698</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_24c9c_level0_row7\" class=\"row_heading level0 row7\" >data/processed/megatron_format/mmistral_cc3m_full_shard_007_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_24c9c_row7_col0\" class=\"data row7 col0\" >118,722,124</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_24c9c_level0_row8\" class=\"row_heading level0 row8\" >data/processed/megatron_format/mmistral_cc3m_full_shard_008_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_24c9c_row8_col0\" class=\"data row8 col0\" >115,394,775</td>\n",
       "      <td id=\"T_24c9c_row8_col1\" class=\"data row8 col1\" >8,247,417</td>\n",
       "      <td id=\"T_24c9c_row8_col2\" class=\"data row8 col2\" >107,147,358</td>\n",
       "      <td id=\"T_24c9c_row8_col3\" class=\"data row8 col3\" >252,751</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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       "<pandas.io.formats.style.Styler at 0x7f8cdb28db40>"
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",
      "text/plain": [
       "<Figure size 1600x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cc3m_full = aggregate_data(data_files, \"mmistral_cc3m_full_shard_\")\n",
    "visualize_stats(cc3m_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Other source datasets:\n",
    "\n",
    "# figureqa = aggregate_data(data_files, \"mmistral_figureqa_pack32k\")\n",
    "# visualize_stats(figureqa)\n",
    "# dvqa = aggregate_data(data_files, \"mmistral_dvqa_pack32k\")\n",
    "# visualize_stats(dvqa)\n",
    "# llavaR = aggregate_data(data_files, \"mmistral_llavaR_pack32k\")\n",
    "# visualize_stats(llavaR)\n",
    "# ocrvqa = aggregate_data(data_files, \"mmistral_ocrvqa_pack32k\")\n",
    "# visualize_stats(ocrvqa)\n",
    "# websight = aggregate_data(data_files, \"mmistral_websight_shard_\")\n",
    "# visualize_stats(websight)\n",
    "# allava_laion4v = aggregate_data(data_files, \"mmistral_allava_laion4v_pack32k\")\n",
    "# visualize_stats(allava_laion4v)\n",
    "# allava_vflan4v = aggregate_data(data_files, \"mmistral_allava_vflan4v_pack32k\")\n",
    "# visualize_stats(allava_vflan4v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 data files found with data_key=mmistral_slimpajama_shard_\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b1ac1_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
       "      <td id=\"T_b1ac1_row1_col0\" class=\"data row1 col0\" >35,947</td>\n",
       "      <td id=\"T_b1ac1_row1_col1\" class=\"data row1 col1\" >0</td>\n",
       "      <td id=\"T_b1ac1_row1_col2\" class=\"data row1 col2\" >0</td>\n",
       "      <td id=\"T_b1ac1_row1_col3\" class=\"data row1 col3\" >35,947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b1ac1_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
       "      <td id=\"T_b1ac1_row2_col0\" class=\"data row2 col0\" >46,029</td>\n",
       "      <td id=\"T_b1ac1_row2_col1\" class=\"data row2 col1\" >0</td>\n",
       "      <td id=\"T_b1ac1_row2_col2\" class=\"data row2 col2\" >0</td>\n",
       "      <td id=\"T_b1ac1_row2_col3\" class=\"data row2 col3\" >46,029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b1ac1_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
       "      <td id=\"T_b1ac1_row3_col0\" class=\"data row3 col0\" >46</td>\n",
       "      <td id=\"T_b1ac1_row3_col1\" class=\"data row3 col1\" >0</td>\n",
       "      <td id=\"T_b1ac1_row3_col2\" class=\"data row3 col2\" >0</td>\n",
       "      <td id=\"T_b1ac1_row3_col3\" class=\"data row3 col3\" >46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b1ac1_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
       "      <td id=\"T_b1ac1_row4_col0\" class=\"data row4 col0\" >26,964</td>\n",
       "      <td id=\"T_b1ac1_row4_col1\" class=\"data row4 col1\" >0</td>\n",
       "      <td id=\"T_b1ac1_row4_col2\" class=\"data row4 col2\" >0</td>\n",
       "      <td id=\"T_b1ac1_row4_col3\" class=\"data row4 col3\" >26,964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b1ac1_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
       "      <td id=\"T_b1ac1_row5_col0\" class=\"data row5 col0\" >31,636</td>\n",
       "      <td id=\"T_b1ac1_row5_col1\" class=\"data row5 col1\" >0</td>\n",
       "      <td id=\"T_b1ac1_row5_col2\" class=\"data row5 col2\" >0</td>\n",
       "      <td id=\"T_b1ac1_row5_col3\" class=\"data row5 col3\" >31,636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b1ac1_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
       "      <td id=\"T_b1ac1_row6_col0\" class=\"data row6 col0\" >32,549</td>\n",
       "      <td id=\"T_b1ac1_row6_col1\" class=\"data row6 col1\" >0</td>\n",
       "      <td id=\"T_b1ac1_row6_col2\" class=\"data row6 col2\" >0</td>\n",
       "      <td id=\"T_b1ac1_row6_col3\" class=\"data row6 col3\" >32,549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b1ac1_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
       "      <td id=\"T_b1ac1_row7_col0\" class=\"data row7 col0\" >4,190,363</td>\n",
       "      <td id=\"T_b1ac1_row7_col1\" class=\"data row7 col1\" >0</td>\n",
       "      <td id=\"T_b1ac1_row7_col2\" class=\"data row7 col2\" >0</td>\n",
       "      <td id=\"T_b1ac1_row7_col3\" class=\"data row7 col3\" >4,190,363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b1ac1_level0_row8\" class=\"row_heading level0 row8\" >total</th>\n",
       "      <td id=\"T_b1ac1_row8_col0\" class=\"data row8 col0\" >4,344,110,111</td>\n",
       "      <td id=\"T_b1ac1_row8_col1\" class=\"data row8 col1\" >0</td>\n",
       "      <td id=\"T_b1ac1_row8_col2\" class=\"data row8 col2\" >0</td>\n",
       "      <td id=\"T_b1ac1_row8_col3\" class=\"data row8 col3\" >4,344,110,111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f8c485ee620>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3201505/3560877793.py:36: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
      "  ax[i].set_xlim(0, stats[metric].max())\n",
      "/tmp/ipykernel_3201505/3560877793.py:36: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
      "  ax[i].set_xlim(0, stats[metric].max())\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slimpajama_shard000 = aggregate_data(data_files, \"mmistral_slimpajama_shard_\", shard_ids={\"000\"})\n",
    "visualize_stats(slimpajama_shard000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 data files found with data_key=mmistral_slimpajama_shard_\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_e8f4b_row0_col0, #T_e8f4b_row0_col1, #T_e8f4b_row0_col2, #T_e8f4b_row0_col3, #T_e8f4b_row1_col1, #T_e8f4b_row1_col2, #T_e8f4b_row2_col1, #T_e8f4b_row2_col2, #T_e8f4b_row3_col1, #T_e8f4b_row3_col2, #T_e8f4b_row4_col1, #T_e8f4b_row4_col2, #T_e8f4b_row5_col1, #T_e8f4b_row5_col2, #T_e8f4b_row6_col1, #T_e8f4b_row6_col2, #T_e8f4b_row7_col1, #T_e8f4b_row7_col2, #T_e8f4b_row8_col1, #T_e8f4b_row8_col2 {\n",
       "  background-color: #440154;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e8f4b_row1_col0, #T_e8f4b_row1_col3, #T_e8f4b_row2_col0, #T_e8f4b_row2_col3, #T_e8f4b_row3_col0, #T_e8f4b_row3_col3, #T_e8f4b_row4_col0, #T_e8f4b_row4_col3, #T_e8f4b_row5_col0, #T_e8f4b_row5_col3, #T_e8f4b_row6_col0, #T_e8f4b_row6_col3, #T_e8f4b_row7_col0, #T_e8f4b_row7_col3, #T_e8f4b_row8_col0, #T_e8f4b_row8_col3 {\n",
       "  background-color: #fde725;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_e8f4b\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_e8f4b_level0_col0\" class=\"col_heading level0 col0\" >n_tokens</th>\n",
       "      <th id=\"T_e8f4b_level0_col1\" class=\"col_heading level0 col1\" >n_image_tokens</th>\n",
       "      <th id=\"T_e8f4b_level0_col2\" class=\"col_heading level0 col2\" >n_images</th>\n",
       "      <th id=\"T_e8f4b_level0_col3\" class=\"col_heading level0 col3\" >n_text_tokens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_e8f4b_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
       "      <td id=\"T_e8f4b_row0_col0\" class=\"data row0 col0\" >120,385</td>\n",
       "      <td id=\"T_e8f4b_row0_col1\" class=\"data row0 col1\" >120,385</td>\n",
       "      <td id=\"T_e8f4b_row0_col2\" class=\"data row0 col2\" >120,385</td>\n",
       "      <td id=\"T_e8f4b_row0_col3\" class=\"data row0 col3\" >120,385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8f4b_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
       "      <td id=\"T_e8f4b_row1_col0\" class=\"data row1 col0\" >35,727</td>\n",
       "      <td id=\"T_e8f4b_row1_col1\" class=\"data row1 col1\" >0</td>\n",
       "      <td id=\"T_e8f4b_row1_col2\" class=\"data row1 col2\" >0</td>\n",
       "      <td id=\"T_e8f4b_row1_col3\" class=\"data row1 col3\" >35,727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8f4b_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
       "      <td id=\"T_e8f4b_row2_col0\" class=\"data row2 col0\" >48,202</td>\n",
       "      <td id=\"T_e8f4b_row2_col1\" class=\"data row2 col1\" >0</td>\n",
       "      <td id=\"T_e8f4b_row2_col2\" class=\"data row2 col2\" >0</td>\n",
       "      <td id=\"T_e8f4b_row2_col3\" class=\"data row2 col3\" >48,202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8f4b_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
       "      <td id=\"T_e8f4b_row3_col0\" class=\"data row3 col0\" >38</td>\n",
       "      <td id=\"T_e8f4b_row3_col1\" class=\"data row3 col1\" >0</td>\n",
       "      <td id=\"T_e8f4b_row3_col2\" class=\"data row3 col2\" >0</td>\n",
       "      <td id=\"T_e8f4b_row3_col3\" class=\"data row3 col3\" >38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8f4b_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
       "      <td id=\"T_e8f4b_row4_col0\" class=\"data row4 col0\" >26,994</td>\n",
       "      <td id=\"T_e8f4b_row4_col1\" class=\"data row4 col1\" >0</td>\n",
       "      <td id=\"T_e8f4b_row4_col2\" class=\"data row4 col2\" >0</td>\n",
       "      <td id=\"T_e8f4b_row4_col3\" class=\"data row4 col3\" >26,994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8f4b_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
       "      <td id=\"T_e8f4b_row5_col0\" class=\"data row5 col0\" >31,646</td>\n",
       "      <td id=\"T_e8f4b_row5_col1\" class=\"data row5 col1\" >0</td>\n",
       "      <td id=\"T_e8f4b_row5_col2\" class=\"data row5 col2\" >0</td>\n",
       "      <td id=\"T_e8f4b_row5_col3\" class=\"data row5 col3\" >31,646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8f4b_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
       "      <td id=\"T_e8f4b_row6_col0\" class=\"data row6 col0\" >32,548</td>\n",
       "      <td id=\"T_e8f4b_row6_col1\" class=\"data row6 col1\" >0</td>\n",
       "      <td id=\"T_e8f4b_row6_col2\" class=\"data row6 col2\" >0</td>\n",
       "      <td id=\"T_e8f4b_row6_col3\" class=\"data row6 col3\" >32,548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8f4b_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
       "      <td id=\"T_e8f4b_row7_col0\" class=\"data row7 col0\" >6,947,482</td>\n",
       "      <td id=\"T_e8f4b_row7_col1\" class=\"data row7 col1\" >0</td>\n",
       "      <td id=\"T_e8f4b_row7_col2\" class=\"data row7 col2\" >0</td>\n",
       "      <td id=\"T_e8f4b_row7_col3\" class=\"data row7 col3\" >6,947,482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e8f4b_level0_row8\" class=\"row_heading level0 row8\" >total</th>\n",
       "      <td id=\"T_e8f4b_row8_col0\" class=\"data row8 col0\" >4,300,998,161</td>\n",
       "      <td id=\"T_e8f4b_row8_col1\" class=\"data row8 col1\" >0</td>\n",
       "      <td id=\"T_e8f4b_row8_col2\" class=\"data row8 col2\" >0</td>\n",
       "      <td id=\"T_e8f4b_row8_col3\" class=\"data row8 col3\" >4,300,998,161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f8c449167d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3201505/3560877793.py:36: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
      "  ax[i].set_xlim(0, stats[metric].max())\n",
      "/tmp/ipykernel_3201505/3560877793.py:36: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
      "  ax[i].set_xlim(0, stats[metric].max())\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slimpajama_shard001 = aggregate_data(data_files, \"mmistral_slimpajama_shard_\", shard_ids={\"001\"})\n",
    "visualize_stats(slimpajama_shard001)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Mixture for Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset1-weight dataset1-path dataset2-weight dataset2-path ...\n",
    "\n",
    "def generate_data_prefix_with_weights(mixture_stats: List[Tuple[pd.DataFrame, float]]):\n",
    "    # assert all weights sum to 1\n",
    "    assert sum([w for _, w in mixture_stats]) == 1.0, \"Weights do not sum to 1\"\n",
    "\n",
    "    ret_mixture_stats = []\n",
    "    for data_stats, weight in mixture_stats:\n",
    "        # calculate the total number of tokens for each data file\n",
    "        n_tokens_by_file = data_stats.groupby(\"filepath\")[[\"n_tokens\", \"n_text_tokens\", \"n_image_tokens\", \"n_images\"]].agg(\n",
    "            n_tokens=(\"n_tokens\", \"sum\"),\n",
    "            n_instances=(\"n_tokens\", \"count\"),\n",
    "            n_text_tokens=(\"n_text_tokens\", \"sum\"),\n",
    "            n_image_tokens=(\"n_image_tokens\", \"sum\"),\n",
    "            n_images=(\"n_images\", \"sum\")\n",
    "        ).reset_index()[[\"filepath\", \"n_tokens\", \"n_instances\", \"n_text_tokens\", \"n_image_tokens\", \"n_images\"]]\n",
    "        # weights are proportional to the number of tokens in each file (ALL SUM TO 1)\n",
    "        n_tokens_by_file[\"weight\"] = n_tokens_by_file[\"n_tokens\"] / n_tokens_by_file[\"n_tokens\"].sum()\n",
    "        # multiply by the weight of the dataset (in the whole mixture)\n",
    "        n_tokens_by_file[\"weight\"] *= weight\n",
    "        ret_mixture_stats.append(n_tokens_by_file)\n",
    "\n",
    "    # concatenate all dataframes and group by filepath to sum the weights\n",
    "    ret_mixture_stats = pd.concat(ret_mixture_stats)\n",
    "\n",
    "    display(ret_mixture_stats.set_index(\"filepath\")[[\"weight\", \"n_instances\", \"n_tokens\", \"n_text_tokens\", \"n_image_tokens\", \"n_images\"]].style.background_gradient(\n",
    "        cmap=\"viridis\", axis=0).format(\"{:,.4f}\", subset=[\"weight\"]).format(\"{:,.0f}\", subset=[\"n_instances\", \"n_tokens\", \"n_text_tokens\", \"n_image_tokens\", \"n_images\"])\n",
    "    )\n",
    "\n",
    "    # calculate final ratio of total text vs. total image tokens\n",
    "    total_text_tokens = ret_mixture_stats[\"n_text_tokens\"].sum()\n",
    "    total_image_tokens = ret_mixture_stats[\"n_image_tokens\"].sum()\n",
    "    total_tokens = total_text_tokens + total_image_tokens\n",
    "    print(f\"# of files: {ret_mixture_stats['filepath'].nunique()}\")\n",
    "    print(f\"Total text tokens: {total_text_tokens:,} ({total_text_tokens / total_tokens:.2%})\")\n",
    "    print(f\"Total image tokens: {total_image_tokens:,} ({total_image_tokens / total_tokens:.2%})\")\n",
    "    print(f\"Total tokens: {total_tokens:,}\")\n",
    "    print(f\"Total instances: ** {ret_mixture_stats['n_instances'].sum()} **\")\n",
    "\n",
    "\n",
    "    # print the final mixture in the right \"dataset\" format\n",
    "    mixture_str = \"\"\n",
    "    for i, row in ret_mixture_stats.iterrows():\n",
    "        # print(f\"{row['weight']:.4f} {row['filepath']}\")\n",
    "        mixture_str += f\" {row['weight']:.4f} {row['filepath'].rstrip('.jsonl')}\"\n",
    "        # print(f\"{row['weight']:.4f} {row['filepath']}\")\n",
    "    print(\"===== FINAL MIXTURE =====\")\n",
    "    print(mixture_str.lstrip())\n",
    "    print(\"=========================\")\n",
    "\n",
    "    return ret_mixture_stats\n",
    "\n",
    "# dataset1-weight dataset1-path dataset2-weight dataset2-path ...\n",
    "\n",
    "def generate_data_prefix_with_natural_weights(mixtures: List[pd.DataFrame]):\n",
    "    ret_mixture_stats = []\n",
    "    for data_stats in mixtures:\n",
    "        # calculate the total number of tokens for each data file\n",
    "        n_tokens_by_file = data_stats.groupby(\"filepath\")[[\"n_tokens\", \"n_text_tokens\", \"n_image_tokens\", \"n_images\"]].agg(\n",
    "            n_tokens=(\"n_tokens\", \"sum\"),\n",
    "            n_instances=(\"n_tokens\", \"count\"),\n",
    "            n_text_tokens=(\"n_text_tokens\", \"sum\"),\n",
    "            n_image_tokens=(\"n_image_tokens\", \"sum\"),\n",
    "            n_images=(\"n_images\", \"sum\")\n",
    "        ).reset_index()[[\"filepath\", \"n_tokens\", \"n_instances\", \"n_text_tokens\", \"n_image_tokens\", \"n_images\"]]\n",
    "        ret_mixture_stats.append(n_tokens_by_file)\n",
    "\n",
    "    # concatenate all dataframes and group by filepath to sum the weights\n",
    "    ret_mixture_stats = pd.concat(ret_mixture_stats)\n",
    "    ret_mixture_stats[\"weight\"] = ret_mixture_stats[\"n_instances\"] / ret_mixture_stats[\"n_instances\"].sum()\n",
    "\n",
    "    display(ret_mixture_stats.set_index(\"filepath\")[[\"weight\", \"n_instances\", \"n_tokens\", \"n_text_tokens\", \"n_image_tokens\", \"n_images\"]].style.background_gradient(\n",
    "        cmap=\"viridis\", axis=0).format(\"{:,.4f}\", subset=[\"weight\"]).format(\"{:,.0f}\", subset=[\"n_instances\", \"n_tokens\", \"n_text_tokens\", \"n_image_tokens\", \"n_images\"])\n",
    "    )\n",
    "\n",
    "    # calculate final ratio of total text vs. total image tokens\n",
    "    total_text_tokens = ret_mixture_stats[\"n_text_tokens\"].sum()\n",
    "    total_image_tokens = ret_mixture_stats[\"n_image_tokens\"].sum()\n",
    "    total_tokens = total_text_tokens + total_image_tokens\n",
    "    print(f\"# of files: {ret_mixture_stats['filepath'].nunique()}\")\n",
    "    print(f\"Total text tokens: {total_text_tokens:,} ({total_text_tokens / total_tokens:.2%})\")\n",
    "    print(f\"Total image tokens: {total_image_tokens:,} ({total_image_tokens / total_tokens:.2%})\")\n",
    "    print(f\"Total tokens: {total_tokens:,}\")\n",
    "    print(f\"Total instances: ** {ret_mixture_stats['n_instances'].sum()} **\")\n",
    "\n",
    "    # aggregate filepaths by removeing shard number\n",
    "    ret_mixture_stats[\"filepath_prefix\"] = ret_mixture_stats[\"filepath\"].apply(lambda x: re.sub(r\"_shard_\\d+\", \"\", x))\n",
    "    display(ret_mixture_stats.groupby(\"filepath_prefix\").agg(\n",
    "        count=(\"filepath\", \"count\"),\n",
    "        weight=(\"weight\", \"sum\"),\n",
    "        n_instances=(\"n_instances\", \"sum\"),\n",
    "        n_tokens=(\"n_tokens\", \"sum\"),\n",
    "        n_text_tokens=(\"n_text_tokens\", \"sum\"),\n",
    "        n_image_tokens=(\"n_image_tokens\", \"sum\"),\n",
    "        n_images=(\"n_images\", \"sum\")\n",
    "    ).sort_values(\"weight\", ascending=False).style.background_gradient(cmap=\"viridis\", axis=0).format(\"{:,.4f}\", subset=[\"weight\"]).format(\"{:,.0f}\", subset=[\"n_instances\", \"n_tokens\", \"n_text_tokens\", \"n_image_tokens\", \"n_images\"]))\n",
    "    \n",
    "\n",
    "    # print the final mixture in the right \"dataset\" format\n",
    "    mixture_str = \"\"\n",
    "    for i, row in ret_mixture_stats.iterrows():\n",
    "        # print(f\"{row['weight']:.4f} {row['filepath']}\")\n",
    "        mixture_str += f\" {row['weight']:.4f} {row['filepath'].rstrip('.jsonl')}\"\n",
    "        # print(f\"{row['weight']:.4f} {row['filepath']}\")\n",
    "    print(\"===== FINAL MIXTURE =====\")\n",
    "    print(mixture_str.lstrip())\n",
    "    print(\"=========================\")\n",
    "\n",
    "    return ret_mixture_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-Stage Pre-training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_capfusion_subset_by_shard_ids(shard_ids):\n",
    "    capfusion_subset = [f for f in capfusion[\"filepath\"].unique() if re.search(r\"shard_(\\d+)\", f).group(1) in shard_ids]\n",
    "    capfusion_train_subset = capfusion[capfusion[\"filepath\"].isin(capfusion_subset)]\n",
    "    return capfusion_train_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stage 1\n",
    "\n",
    "train_mixture_v5_imagenet21k = generate_data_prefix_with_weights(\n",
    "    [\n",
    "        [imagenet21k, 0.7],\n",
    "        [slimpajama_shard002, 0.3],\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
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       "  color: #f1f1f1;\n",
       "}\n",
       "#T_384e4_row44_col5, #T_384e4_row45_col5, #T_384e4_row46_col5, #T_384e4_row47_col5, #T_384e4_row48_col5, #T_384e4_row49_col5, #T_384e4_row50_col5, #T_384e4_row51_col5, #T_384e4_row52_col5, #T_384e4_row53_col5, #T_384e4_row54_col5, #T_384e4_row55_col5, #T_384e4_row56_col5, #T_384e4_row57_col5, #T_384e4_row58_col5, #T_384e4_row59_col5, #T_384e4_row60_col5, #T_384e4_row61_col5, #T_384e4_row62_col5 {\n",
       "  background-color: #472e7c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_384e4_row49_col4, #T_384e4_row57_col4, #T_384e4_row61_col4 {\n",
       "  background-color: #2f6b8e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_384e4_row63_col4 {\n",
       "  background-color: #31678e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_384e4_row63_col5 {\n",
       "  background-color: #472c7a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_384e4_row64_col4 {\n",
       "  background-color: #433d84;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_384e4_row64_col5, #T_384e4_row65_col4 {\n",
       "  background-color: #3e4a89;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_384e4_row65_col5 {\n",
       "  background-color: #38588c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_384e4_row66_col4 {\n",
       "  background-color: #46327e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_384e4_row66_col5 {\n",
       "  background-color: #472f7d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_384e4\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_384e4_level0_col0\" class=\"col_heading level0 col0\" >weight</th>\n",
       "      <th id=\"T_384e4_level0_col1\" class=\"col_heading level0 col1\" >n_instances</th>\n",
       "      <th id=\"T_384e4_level0_col2\" class=\"col_heading level0 col2\" >n_tokens</th>\n",
       "      <th id=\"T_384e4_level0_col3\" class=\"col_heading level0 col3\" >n_text_tokens</th>\n",
       "      <th id=\"T_384e4_level0_col4\" class=\"col_heading level0 col4\" >n_image_tokens</th>\n",
       "      <th id=\"T_384e4_level0_col5\" class=\"col_heading level0 col5\" >n_images</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >filepath</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row0\" class=\"row_heading level0 row0\" >data/processed/megatron_format/mmistral_capfusion_shard_000_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row0_col0\" class=\"data row0 col0\" >0.0143</td>\n",
       "      <td id=\"T_384e4_row0_col1\" class=\"data row0 col1\" >6,392</td>\n",
       "      <td id=\"T_384e4_row0_col2\" class=\"data row0 col2\" >207,815,782</td>\n",
       "      <td id=\"T_384e4_row0_col3\" class=\"data row0 col3\" >36,655,853</td>\n",
       "      <td id=\"T_384e4_row0_col4\" class=\"data row0 col4\" >171,159,929</td>\n",
       "      <td id=\"T_384e4_row0_col5\" class=\"data row0 col5\" >740,163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row1\" class=\"row_heading level0 row1\" >data/processed/megatron_format/mmistral_capfusion_shard_001_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row1_col0\" class=\"data row1 col0\" >0.0143</td>\n",
       "      <td id=\"T_384e4_row1_col1\" class=\"data row1 col1\" >6,360</td>\n",
       "      <td id=\"T_384e4_row1_col2\" class=\"data row1 col2\" >206,774,364</td>\n",
       "      <td id=\"T_384e4_row1_col3\" class=\"data row1 col3\" >36,571,627</td>\n",
       "      <td id=\"T_384e4_row1_col4\" class=\"data row1 col4\" >170,202,737</td>\n",
       "      <td id=\"T_384e4_row1_col5\" class=\"data row1 col5\" >739,583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row2\" class=\"row_heading level0 row2\" >data/processed/megatron_format/mmistral_capfusion_shard_002_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row2_col0\" class=\"data row2 col0\" >0.0144</td>\n",
       "      <td id=\"T_384e4_row2_col1\" class=\"data row2 col1\" >6,417</td>\n",
       "      <td id=\"T_384e4_row2_col2\" class=\"data row2 col2\" >208,615,428</td>\n",
       "      <td id=\"T_384e4_row2_col3\" class=\"data row2 col3\" >36,747,426</td>\n",
       "      <td id=\"T_384e4_row2_col4\" class=\"data row2 col4\" >171,868,002</td>\n",
       "      <td id=\"T_384e4_row2_col5\" class=\"data row2 col5\" >740,771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row3\" class=\"row_heading level0 row3\" >data/processed/megatron_format/mmistral_capfusion_shard_003_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row3_col0\" class=\"data row3 col0\" >0.0142</td>\n",
       "      <td id=\"T_384e4_row3_col1\" class=\"data row3 col1\" >6,343</td>\n",
       "      <td id=\"T_384e4_row3_col2\" class=\"data row3 col2\" >206,227,749</td>\n",
       "      <td id=\"T_384e4_row3_col3\" class=\"data row3 col3\" >36,568,754</td>\n",
       "      <td id=\"T_384e4_row3_col4\" class=\"data row3 col4\" >169,658,995</td>\n",
       "      <td id=\"T_384e4_row3_col5\" class=\"data row3 col5\" >739,404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row4\" class=\"row_heading level0 row4\" >data/processed/megatron_format/mmistral_capfusion_shard_004_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row4_col0\" class=\"data row4 col0\" >0.0144</td>\n",
       "      <td id=\"T_384e4_row4_col1\" class=\"data row4 col1\" >6,426</td>\n",
       "      <td id=\"T_384e4_row4_col2\" class=\"data row4 col2\" >208,920,072</td>\n",
       "      <td id=\"T_384e4_row4_col3\" class=\"data row4 col3\" >36,745,513</td>\n",
       "      <td id=\"T_384e4_row4_col4\" class=\"data row4 col4\" >172,174,559</td>\n",
       "      <td id=\"T_384e4_row4_col5\" class=\"data row4 col5\" >741,079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row5\" class=\"row_heading level0 row5\" >data/processed/megatron_format/mmistral_capfusion_shard_005_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row5_col0\" class=\"data row5 col0\" >0.0146</td>\n",
       "      <td id=\"T_384e4_row5_col1\" class=\"data row5 col1\" >6,485</td>\n",
       "      <td id=\"T_384e4_row5_col2\" class=\"data row5 col2\" >210,832,912</td>\n",
       "      <td id=\"T_384e4_row5_col3\" class=\"data row5 col3\" >36,765,302</td>\n",
       "      <td id=\"T_384e4_row5_col4\" class=\"data row5 col4\" >174,067,610</td>\n",
       "      <td id=\"T_384e4_row5_col5\" class=\"data row5 col5\" >741,748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row6\" class=\"row_heading level0 row6\" >data/processed/megatron_format/mmistral_capfusion_shard_006_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row6_col0\" class=\"data row6 col0\" >0.0144</td>\n",
       "      <td id=\"T_384e4_row6_col1\" class=\"data row6 col1\" >6,395</td>\n",
       "      <td id=\"T_384e4_row6_col2\" class=\"data row6 col2\" >207,911,194</td>\n",
       "      <td id=\"T_384e4_row6_col3\" class=\"data row6 col3\" >36,609,107</td>\n",
       "      <td id=\"T_384e4_row6_col4\" class=\"data row6 col4\" >171,302,087</td>\n",
       "      <td id=\"T_384e4_row6_col5\" class=\"data row6 col5\" >739,923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row7\" class=\"row_heading level0 row7\" >data/processed/megatron_format/mmistral_capfusion_shard_007_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row7_col0\" class=\"data row7 col0\" >0.0144</td>\n",
       "      <td id=\"T_384e4_row7_col1\" class=\"data row7 col1\" >6,407</td>\n",
       "      <td id=\"T_384e4_row7_col2\" class=\"data row7 col2\" >208,287,357</td>\n",
       "      <td id=\"T_384e4_row7_col3\" class=\"data row7 col3\" >36,663,998</td>\n",
       "      <td id=\"T_384e4_row7_col4\" class=\"data row7 col4\" >171,623,359</td>\n",
       "      <td id=\"T_384e4_row7_col5\" class=\"data row7 col5\" >740,591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row8\" class=\"row_heading level0 row8\" >data/processed/megatron_format/mmistral_capfusion_shard_008_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row8_col0\" class=\"data row8 col0\" >0.0145</td>\n",
       "      <td id=\"T_384e4_row8_col1\" class=\"data row8 col1\" >6,477</td>\n",
       "      <td id=\"T_384e4_row8_col2\" class=\"data row8 col2\" >210,584,435</td>\n",
       "      <td id=\"T_384e4_row8_col3\" class=\"data row8 col3\" >36,796,178</td>\n",
       "      <td id=\"T_384e4_row8_col4\" class=\"data row8 col4\" >173,788,257</td>\n",
       "      <td id=\"T_384e4_row8_col5\" class=\"data row8 col5\" >741,689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row9\" class=\"row_heading level0 row9\" >data/processed/megatron_format/mmistral_capfusion_shard_009_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row9_col0\" class=\"data row9 col0\" >0.0143</td>\n",
       "      <td id=\"T_384e4_row9_col1\" class=\"data row9 col1\" >6,356</td>\n",
       "      <td id=\"T_384e4_row9_col2\" class=\"data row9 col2\" >206,667,352</td>\n",
       "      <td id=\"T_384e4_row9_col3\" class=\"data row9 col3\" >36,681,217</td>\n",
       "      <td id=\"T_384e4_row9_col4\" class=\"data row9 col4\" >169,986,135</td>\n",
       "      <td id=\"T_384e4_row9_col5\" class=\"data row9 col5\" >740,539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row10\" class=\"row_heading level0 row10\" >data/processed/megatron_format/mmistral_capfusion_shard_010_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row10_col0\" class=\"data row10 col0\" >0.0146</td>\n",
       "      <td id=\"T_384e4_row10_col1\" class=\"data row10 col1\" >6,488</td>\n",
       "      <td id=\"T_384e4_row10_col2\" class=\"data row10 col2\" >210,939,494</td>\n",
       "      <td id=\"T_384e4_row10_col3\" class=\"data row10 col3\" >36,707,229</td>\n",
       "      <td id=\"T_384e4_row10_col4\" class=\"data row10 col4\" >174,232,265</td>\n",
       "      <td id=\"T_384e4_row10_col5\" class=\"data row10 col5\" >740,782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row11\" class=\"row_heading level0 row11\" >data/processed/megatron_format/mmistral_capfusion_shard_011_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row11_col0\" class=\"data row11 col0\" >0.0142</td>\n",
       "      <td id=\"T_384e4_row11_col1\" class=\"data row11 col1\" >6,305</td>\n",
       "      <td id=\"T_384e4_row11_col2\" class=\"data row11 col2\" >204,985,995</td>\n",
       "      <td id=\"T_384e4_row11_col3\" class=\"data row11 col3\" >36,482,893</td>\n",
       "      <td id=\"T_384e4_row11_col4\" class=\"data row11 col4\" >168,503,102</td>\n",
       "      <td id=\"T_384e4_row11_col5\" class=\"data row11 col5\" >738,883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row12\" class=\"row_heading level0 row12\" >data/processed/megatron_format/mmistral_capfusion_shard_012_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row12_col0\" class=\"data row12 col0\" >0.0145</td>\n",
       "      <td id=\"T_384e4_row12_col1\" class=\"data row12 col1\" >6,453</td>\n",
       "      <td id=\"T_384e4_row12_col2\" class=\"data row12 col2\" >209,796,060</td>\n",
       "      <td id=\"T_384e4_row12_col3\" class=\"data row12 col3\" >36,799,784</td>\n",
       "      <td id=\"T_384e4_row12_col4\" class=\"data row12 col4\" >172,996,276</td>\n",
       "      <td id=\"T_384e4_row12_col5\" class=\"data row12 col5\" >741,327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row13\" class=\"row_heading level0 row13\" >data/processed/megatron_format/mmistral_capfusion_shard_013_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row13_col0\" class=\"data row13 col0\" >0.0144</td>\n",
       "      <td id=\"T_384e4_row13_col1\" class=\"data row13 col1\" >6,404</td>\n",
       "      <td id=\"T_384e4_row13_col2\" class=\"data row13 col2\" >208,169,220</td>\n",
       "      <td id=\"T_384e4_row13_col3\" class=\"data row13 col3\" >36,571,537</td>\n",
       "      <td id=\"T_384e4_row13_col4\" class=\"data row13 col4\" >171,597,683</td>\n",
       "      <td id=\"T_384e4_row13_col5\" class=\"data row13 col5\" >738,966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row14\" class=\"row_heading level0 row14\" >data/processed/megatron_format/mmistral_capfusion_shard_014_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row14_col0\" class=\"data row14 col0\" >0.0144</td>\n",
       "      <td id=\"T_384e4_row14_col1\" class=\"data row14 col1\" >6,395</td>\n",
       "      <td id=\"T_384e4_row14_col2\" class=\"data row14 col2\" >207,921,445</td>\n",
       "      <td id=\"T_384e4_row14_col3\" class=\"data row14 col3\" >36,632,592</td>\n",
       "      <td id=\"T_384e4_row14_col4\" class=\"data row14 col4\" >171,288,853</td>\n",
       "      <td id=\"T_384e4_row14_col5\" class=\"data row14 col5\" >739,122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row15\" class=\"row_heading level0 row15\" >data/processed/megatron_format/mmistral_capfusion_shard_015_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row15_col0\" class=\"data row15 col0\" >0.0145</td>\n",
       "      <td id=\"T_384e4_row15_col1\" class=\"data row15 col1\" >6,446</td>\n",
       "      <td id=\"T_384e4_row15_col2\" class=\"data row15 col2\" >209,603,298</td>\n",
       "      <td id=\"T_384e4_row15_col3\" class=\"data row15 col3\" >36,662,340</td>\n",
       "      <td id=\"T_384e4_row15_col4\" class=\"data row15 col4\" >172,940,958</td>\n",
       "      <td id=\"T_384e4_row15_col5\" class=\"data row15 col5\" >739,598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row16\" class=\"row_heading level0 row16\" >data/processed/megatron_format/mmistral_capfusion_shard_016_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row16_col0\" class=\"data row16 col0\" >0.0145</td>\n",
       "      <td id=\"T_384e4_row16_col1\" class=\"data row16 col1\" >6,455</td>\n",
       "      <td id=\"T_384e4_row16_col2\" class=\"data row16 col2\" >209,858,056</td>\n",
       "      <td id=\"T_384e4_row16_col3\" class=\"data row16 col3\" >36,730,258</td>\n",
       "      <td id=\"T_384e4_row16_col4\" class=\"data row16 col4\" >173,127,798</td>\n",
       "      <td id=\"T_384e4_row16_col5\" class=\"data row16 col5\" >740,990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row17\" class=\"row_heading level0 row17\" >data/processed/megatron_format/mmistral_capfusion_shard_017_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row17_col0\" class=\"data row17 col0\" >0.0144</td>\n",
       "      <td id=\"T_384e4_row17_col1\" class=\"data row17 col1\" >6,417</td>\n",
       "      <td id=\"T_384e4_row17_col2\" class=\"data row17 col2\" >208,641,487</td>\n",
       "      <td id=\"T_384e4_row17_col3\" class=\"data row17 col3\" >36,694,177</td>\n",
       "      <td id=\"T_384e4_row17_col4\" class=\"data row17 col4\" >171,947,310</td>\n",
       "      <td id=\"T_384e4_row17_col5\" class=\"data row17 col5\" >740,927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row18\" class=\"row_heading level0 row18\" >data/processed/megatron_format/mmistral_capfusion_shard_018_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row18_col0\" class=\"data row18 col0\" >0.0143</td>\n",
       "      <td id=\"T_384e4_row18_col1\" class=\"data row18 col1\" >6,359</td>\n",
       "      <td id=\"T_384e4_row18_col2\" class=\"data row18 col2\" >206,740,778</td>\n",
       "      <td id=\"T_384e4_row18_col3\" class=\"data row18 col3\" >36,567,322</td>\n",
       "      <td id=\"T_384e4_row18_col4\" class=\"data row18 col4\" >170,173,456</td>\n",
       "      <td id=\"T_384e4_row18_col5\" class=\"data row18 col5\" >739,135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row19\" class=\"row_heading level0 row19\" >data/processed/megatron_format/mmistral_capfusion_shard_019_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row19_col0\" class=\"data row19 col0\" >0.0146</td>\n",
       "      <td id=\"T_384e4_row19_col1\" class=\"data row19 col1\" >6,494</td>\n",
       "      <td id=\"T_384e4_row19_col2\" class=\"data row19 col2\" >211,107,993</td>\n",
       "      <td id=\"T_384e4_row19_col3\" class=\"data row19 col3\" >36,796,316</td>\n",
       "      <td id=\"T_384e4_row19_col4\" class=\"data row19 col4\" >174,311,677</td>\n",
       "      <td id=\"T_384e4_row19_col5\" class=\"data row19 col5\" >741,722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row20\" class=\"row_heading level0 row20\" >data/processed/megatron_format/mmistral_capfusion_shard_020_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row20_col0\" class=\"data row20 col0\" >0.0143</td>\n",
       "      <td id=\"T_384e4_row20_col1\" class=\"data row20 col1\" >6,391</td>\n",
       "      <td id=\"T_384e4_row20_col2\" class=\"data row20 col2\" >207,766,784</td>\n",
       "      <td id=\"T_384e4_row20_col3\" class=\"data row20 col3\" >36,588,966</td>\n",
       "      <td id=\"T_384e4_row20_col4\" class=\"data row20 col4\" >171,177,818</td>\n",
       "      <td id=\"T_384e4_row20_col5\" class=\"data row20 col5\" >738,767</td>\n",
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       "    <tr>\n",
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       "      <th id=\"T_384e4_level0_row53\" class=\"row_heading level0 row53\" >data/processed/megatron_format/mmistral_websight_shard_009_pack32k/data.jsonl</th>\n",
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       "      <th id=\"T_384e4_level0_row54\" class=\"row_heading level0 row54\" >data/processed/megatron_format/mmistral_websight_shard_010_pack32k/data.jsonl</th>\n",
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       "      <th id=\"T_384e4_level0_row56\" class=\"row_heading level0 row56\" >data/processed/megatron_format/mmistral_websight_shard_012_pack32k/data.jsonl</th>\n",
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       "      <th id=\"T_384e4_level0_row57\" class=\"row_heading level0 row57\" >data/processed/megatron_format/mmistral_websight_shard_013_pack32k/data.jsonl</th>\n",
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       "      <th id=\"T_384e4_level0_row58\" class=\"row_heading level0 row58\" >data/processed/megatron_format/mmistral_websight_shard_014_pack32k/data.jsonl</th>\n",
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       "      <th id=\"T_384e4_level0_row61\" class=\"row_heading level0 row61\" >data/processed/megatron_format/mmistral_websight_shard_017_pack32k/data.jsonl</th>\n",
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       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row62\" class=\"row_heading level0 row62\" >data/processed/megatron_format/mmistral_websight_shard_018_pack32k/data.jsonl</th>\n",
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       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row63\" class=\"row_heading level0 row63\" >data/processed/megatron_format/mmistral_websight_shard_019_pack32k/data.jsonl</th>\n",
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       "      <td id=\"T_384e4_row63_col5\" class=\"data row63 col5\" >91,180</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row64\" class=\"row_heading level0 row64\" >data/processed/megatron_format/mmistral_ocrvqa_pack32k/data.jsonl</th>\n",
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       "      <td id=\"T_384e4_row64_col1\" class=\"data row64 col1\" >1,593</td>\n",
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       "      <td id=\"T_384e4_row64_col3\" class=\"data row64 col3\" >21,161,018</td>\n",
       "      <td id=\"T_384e4_row64_col4\" class=\"data row64 col4\" >30,759,687</td>\n",
       "      <td id=\"T_384e4_row64_col5\" class=\"data row64 col5\" >165,746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row65\" class=\"row_heading level0 row65\" >data/processed/megatron_format/mmistral_dvqa_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row65_col0\" class=\"data row65 col0\" >0.0065</td>\n",
       "      <td id=\"T_384e4_row65_col1\" class=\"data row65 col1\" >2,917</td>\n",
       "      <td id=\"T_384e4_row65_col2\" class=\"data row65 col2\" >94,853,796</td>\n",
       "      <td id=\"T_384e4_row65_col3\" class=\"data row65 col3\" >55,653,796</td>\n",
       "      <td id=\"T_384e4_row65_col4\" class=\"data row65 col4\" >39,200,000</td>\n",
       "      <td id=\"T_384e4_row65_col5\" class=\"data row65 col5\" >200,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_384e4_level0_row66\" class=\"row_heading level0 row66\" >data/processed/megatron_format/mmistral_figureqa_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_384e4_row66_col0\" class=\"data row66 col0\" >0.0034</td>\n",
       "      <td id=\"T_384e4_row66_col1\" class=\"data row66 col1\" >1,526</td>\n",
       "      <td id=\"T_384e4_row66_col2\" class=\"data row66 col2\" >49,586,305</td>\n",
       "      <td id=\"T_384e4_row66_col3\" class=\"data row66 col3\" >24,803,256</td>\n",
       "      <td id=\"T_384e4_row66_col4\" class=\"data row66 col4\" >24,783,049</td>\n",
       "      <td id=\"T_384e4_row66_col5\" class=\"data row66 col5\" >100,000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9de2f6feb0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of files: 67\n",
      "Total text tokens: 6,814,674,150 (45.90%)\n",
      "Total image tokens: 8,031,924,763 (54.10%)\n",
      "Total tokens: 14,846,598,913\n",
      "Total instances: ** 445565 **\n"
     ]
    },
    {
     "data": {
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       "#T_cae5b_row3_col4, #T_cae5b_row4_col6 {\n",
       "  background-color: #450559;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cae5b_row3_col5 {\n",
       "  background-color: #433e85;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cae5b_row3_col6 {\n",
       "  background-color: #482475;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cae5b_row4_col1, #T_cae5b_row4_col2, #T_cae5b_row4_col3 {\n",
       "  background-color: #46085c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cae5b_row4_col4, #T_cae5b_row6_col1, #T_cae5b_row6_col2, #T_cae5b_row6_col3, #T_cae5b_row6_col5, #T_cae5b_row7_col5, #T_cae5b_row7_col6, #T_cae5b_row8_col5, #T_cae5b_row8_col6 {\n",
       "  background-color: #440256;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cae5b_row4_col5 {\n",
       "  background-color: #460b5e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cae5b_row5_col1, #T_cae5b_row5_col2, #T_cae5b_row5_col3, #T_cae5b_row6_col4, #T_cae5b_row6_col6 {\n",
       "  background-color: #450457;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cae5b_row5_col5, #T_cae5b_row5_col6 {\n",
       "  background-color: #46075a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_cae5b\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_cae5b_level0_col0\" class=\"col_heading level0 col0\" >count</th>\n",
       "      <th id=\"T_cae5b_level0_col1\" class=\"col_heading level0 col1\" >weight</th>\n",
       "      <th id=\"T_cae5b_level0_col2\" class=\"col_heading level0 col2\" >n_instances</th>\n",
       "      <th id=\"T_cae5b_level0_col3\" class=\"col_heading level0 col3\" >n_tokens</th>\n",
       "      <th id=\"T_cae5b_level0_col4\" class=\"col_heading level0 col4\" >n_text_tokens</th>\n",
       "      <th id=\"T_cae5b_level0_col5\" class=\"col_heading level0 col5\" >n_image_tokens</th>\n",
       "      <th id=\"T_cae5b_level0_col6\" class=\"col_heading level0 col6\" >n_images</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >filepath_prefix</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_cae5b_level0_row0\" class=\"row_heading level0 row0\" >data/processed/megatron_format/mmistral_capfusion_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_cae5b_row0_col0\" class=\"data row0 col0\" >32</td>\n",
       "      <td id=\"T_cae5b_row0_col1\" class=\"data row0 col1\" >0.4600</td>\n",
       "      <td id=\"T_cae5b_row0_col2\" class=\"data row0 col2\" >204,978</td>\n",
       "      <td id=\"T_cae5b_row0_col3\" class=\"data row0 col3\" >6,664,351,863</td>\n",
       "      <td id=\"T_cae5b_row0_col4\" class=\"data row0 col4\" >1,172,726,505</td>\n",
       "      <td id=\"T_cae5b_row0_col5\" class=\"data row0 col5\" >5,491,625,358</td>\n",
       "      <td id=\"T_cae5b_row0_col6\" class=\"data row0 col6\" >23,681,864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cae5b_level0_row1\" class=\"row_heading level0 row1\" >data/processed/megatron_format/mmistral_slimpajama_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_cae5b_row1_col0\" class=\"data row1 col0\" >1</td>\n",
       "      <td id=\"T_cae5b_row1_col1\" class=\"data row1 col1\" >0.2702</td>\n",
       "      <td id=\"T_cae5b_row1_col2\" class=\"data row1 col2\" >120,385</td>\n",
       "      <td id=\"T_cae5b_row1_col3\" class=\"data row1 col3\" >4,300,998,161</td>\n",
       "      <td id=\"T_cae5b_row1_col4\" class=\"data row1 col4\" >4,300,998,161</td>\n",
       "      <td id=\"T_cae5b_row1_col5\" class=\"data row1 col5\" >0</td>\n",
       "      <td id=\"T_cae5b_row1_col6\" class=\"data row1 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cae5b_level0_row2\" class=\"row_heading level0 row2\" >data/processed/megatron_format/mmistral_websight_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_cae5b_row2_col0\" class=\"data row2 col0\" >20</td>\n",
       "      <td id=\"T_cae5b_row2_col1\" class=\"data row2 col1\" >0.1606</td>\n",
       "      <td id=\"T_cae5b_row2_col2\" class=\"data row2 col2\" >71,579</td>\n",
       "      <td id=\"T_cae5b_row2_col3\" class=\"data row2 col3\" >2,300,945,215</td>\n",
       "      <td id=\"T_cae5b_row2_col4\" class=\"data row2 col4\" >1,087,060,511</td>\n",
       "      <td id=\"T_cae5b_row2_col5\" class=\"data row2 col5\" >1,213,884,704</td>\n",
       "      <td id=\"T_cae5b_row2_col6\" class=\"data row2 col6\" >1,922,671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cae5b_level0_row3\" class=\"row_heading level0 row3\" >data/processed/megatron_format/mmistral_cc3m_full_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_cae5b_row3_col0\" class=\"data row3 col0\" >9</td>\n",
       "      <td id=\"T_cae5b_row3_col1\" class=\"data row3 col1\" >0.0735</td>\n",
       "      <td id=\"T_cae5b_row3_col2\" class=\"data row3 col2\" >32,760</td>\n",
       "      <td id=\"T_cae5b_row3_col3\" class=\"data row3 col3\" >1,064,477,314</td>\n",
       "      <td id=\"T_cae5b_row3_col4\" class=\"data row3 col4\" >76,092,147</td>\n",
       "      <td id=\"T_cae5b_row3_col5\" class=\"data row3 col5\" >988,385,167</td>\n",
       "      <td id=\"T_cae5b_row3_col6\" class=\"data row3 col6\" >2,331,439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cae5b_level0_row4\" class=\"row_heading level0 row4\" >data/processed/megatron_format/mmistral_detailed_captions_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_cae5b_row4_col0\" class=\"data row4 col0\" >1</td>\n",
       "      <td id=\"T_cae5b_row4_col1\" class=\"data row4 col1\" >0.0140</td>\n",
       "      <td id=\"T_cae5b_row4_col2\" class=\"data row4 col2\" >6,225</td>\n",
       "      <td id=\"T_cae5b_row4_col3\" class=\"data row4 col3\" >202,016,770</td>\n",
       "      <td id=\"T_cae5b_row4_col4\" class=\"data row4 col4\" >44,788,200</td>\n",
       "      <td id=\"T_cae5b_row4_col5\" class=\"data row4 col5\" >157,228,570</td>\n",
       "      <td id=\"T_cae5b_row4_col6\" class=\"data row4 col6\" >368,767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cae5b_level0_row5\" class=\"row_heading level0 row5\" >data/processed/megatron_format/mmistral_llavaR_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_cae5b_row5_col0\" class=\"data row5 col0\" >1</td>\n",
       "      <td id=\"T_cae5b_row5_col1\" class=\"data row5 col1\" >0.0081</td>\n",
       "      <td id=\"T_cae5b_row5_col2\" class=\"data row5 col2\" >3,602</td>\n",
       "      <td id=\"T_cae5b_row5_col3\" class=\"data row5 col3\" >117,448,784</td>\n",
       "      <td id=\"T_cae5b_row5_col4\" class=\"data row5 col4\" >31,390,556</td>\n",
       "      <td id=\"T_cae5b_row5_col5\" class=\"data row5 col5\" >86,058,228</td>\n",
       "      <td id=\"T_cae5b_row5_col6\" class=\"data row5 col6\" >422,315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cae5b_level0_row6\" class=\"row_heading level0 row6\" >data/processed/megatron_format/mmistral_dvqa_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_cae5b_row6_col0\" class=\"data row6 col0\" >1</td>\n",
       "      <td id=\"T_cae5b_row6_col1\" class=\"data row6 col1\" >0.0065</td>\n",
       "      <td id=\"T_cae5b_row6_col2\" class=\"data row6 col2\" >2,917</td>\n",
       "      <td id=\"T_cae5b_row6_col3\" class=\"data row6 col3\" >94,853,796</td>\n",
       "      <td id=\"T_cae5b_row6_col4\" class=\"data row6 col4\" >55,653,796</td>\n",
       "      <td id=\"T_cae5b_row6_col5\" class=\"data row6 col5\" >39,200,000</td>\n",
       "      <td id=\"T_cae5b_row6_col6\" class=\"data row6 col6\" >200,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cae5b_level0_row7\" class=\"row_heading level0 row7\" >data/processed/megatron_format/mmistral_ocrvqa_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_cae5b_row7_col0\" class=\"data row7 col0\" >1</td>\n",
       "      <td id=\"T_cae5b_row7_col1\" class=\"data row7 col1\" >0.0036</td>\n",
       "      <td id=\"T_cae5b_row7_col2\" class=\"data row7 col2\" >1,593</td>\n",
       "      <td id=\"T_cae5b_row7_col3\" class=\"data row7 col3\" >51,920,705</td>\n",
       "      <td id=\"T_cae5b_row7_col4\" class=\"data row7 col4\" >21,161,018</td>\n",
       "      <td id=\"T_cae5b_row7_col5\" class=\"data row7 col5\" >30,759,687</td>\n",
       "      <td id=\"T_cae5b_row7_col6\" class=\"data row7 col6\" >165,746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cae5b_level0_row8\" class=\"row_heading level0 row8\" >data/processed/megatron_format/mmistral_figureqa_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_cae5b_row8_col0\" class=\"data row8 col0\" >1</td>\n",
       "      <td id=\"T_cae5b_row8_col1\" class=\"data row8 col1\" >0.0034</td>\n",
       "      <td id=\"T_cae5b_row8_col2\" class=\"data row8 col2\" >1,526</td>\n",
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       "      <td id=\"T_cae5b_row8_col6\" class=\"data row8 col6\" >100,000</td>\n",
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       "</table>\n"
      ],
      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===== FINAL MIXTURE =====\n",
      "0.0143 data/processed/megatron_format/mmistral_capfusion_shard_000_pack32k/data 0.0143 data/processed/megatron_format/mmistral_capfusion_shard_001_pack32k/data 0.0144 data/processed/megatron_format/mmistral_capfusion_shard_002_pack32k/data 0.0142 data/processed/megatron_format/mmistral_capfusion_shard_003_pack32k/data 0.0144 data/processed/megatron_format/mmistral_capfusion_shard_004_pack32k/data 0.0146 data/processed/megatron_format/mmistral_capfusion_shard_005_pack32k/data 0.0144 data/processed/megatron_format/mmistral_capfusion_shard_006_pack32k/data 0.0144 data/processed/megatron_format/mmistral_capfusion_shard_007_pack32k/data 0.0145 data/processed/megatron_format/mmistral_capfusion_shard_008_pack32k/data 0.0143 data/processed/megatron_format/mmistral_capfusion_shard_009_pack32k/data 0.0146 data/processed/megatron_format/mmistral_capfusion_shard_010_pack32k/data 0.0142 data/processed/megatron_format/mmistral_capfusion_shard_011_pack32k/data 0.0145 data/processed/megatron_format/mmistral_capfusion_shard_012_pack32k/data 0.0144 data/processed/megatron_format/mmistral_capfusion_shard_013_pack32k/data 0.0144 data/processed/megatron_format/mmistral_capfusion_shard_014_pack32k/data 0.0145 data/processed/megatron_format/mmistral_capfusion_shard_015_pack32k/data 0.0145 data/processed/megatron_format/mmistral_capfusion_shard_016_pack32k/data 0.0144 data/processed/megatron_format/mmistral_capfusion_shard_017_pack32k/data 0.0143 data/processed/megatron_format/mmistral_capfusion_shard_018_pack32k/data 0.0146 data/processed/megatron_format/mmistral_capfusion_shard_019_pack32k/data 0.0143 data/processed/megatron_format/mmistral_capfusion_shard_020_pack32k/data 0.0143 data/processed/megatron_format/mmistral_capfusion_shard_021_pack32k/data 0.0145 data/processed/megatron_format/mmistral_capfusion_shard_022_pack32k/data 0.0142 data/processed/megatron_format/mmistral_capfusion_shard_023_pack32k/data 0.0143 data/processed/megatron_format/mmistral_capfusion_shard_024_pack32k/data 0.0145 data/processed/megatron_format/mmistral_capfusion_shard_025_pack32k/data 0.0144 data/processed/megatron_format/mmistral_capfusion_shard_026_pack32k/data 0.0142 data/processed/megatron_format/mmistral_capfusion_shard_027_pack32k/data 0.0144 data/processed/megatron_format/mmistral_capfusion_shard_028_pack32k/data 0.0144 data/processed/megatron_format/mmistral_capfusion_shard_029_pack32k/data 0.0142 data/processed/megatron_format/mmistral_capfusion_shard_030_pack32k/data 0.0145 data/processed/megatron_format/mmistral_capfusion_shard_031_pack32k/data 0.0140 data/processed/megatron_format/mmistral_detailed_captions_shard_000_pack32k/data 0.0082 data/processed/megatron_format/mmistral_cc3m_full_shard_000_pack32k/data 0.0082 data/processed/megatron_format/mmistral_cc3m_full_shard_001_pack32k/data 0.0082 data/processed/megatron_format/mmistral_cc3m_full_shard_002_pack32k/data 0.0082 data/processed/megatron_format/mmistral_cc3m_full_shard_003_pack32k/data 0.0082 data/processed/megatron_format/mmistral_cc3m_full_shard_004_pack32k/data 0.0082 data/processed/megatron_format/mmistral_cc3m_full_shard_005_pack32k/data 0.0082 data/processed/megatron_format/mmistral_cc3m_full_shard_006_pack32k/data 0.0082 data/processed/megatron_format/mmistral_cc3m_full_shard_007_pack32k/data 0.0080 data/processed/megatron_format/mmistral_cc3m_full_shard_008_pack32k/data 0.2702 data/processed/megatron_format/mmistral_slimpajama_shard_001_pack32k/data 0.0081 data/processed/megatron_format/mmistral_llavaR_pack32k/data 0.0081 data/processed/megatron_format/mmistral_websight_shard_000_pack32k/data 0.0081 data/processed/megatron_format/mmistral_websight_shard_001_pack32k/data 0.0080 data/processed/megatron_format/mmistral_websight_shard_002_pack32k/data 0.0080 data/processed/megatron_format/mmistral_websight_shard_003_pack32k/data 0.0080 data/processed/megatron_format/mmistral_websight_shard_004_pack32k/data 0.0079 data/processed/megatron_format/mmistral_websight_shard_005_pack32k/data 0.0081 data/processed/megatron_format/mmistral_websight_shard_006_pack32k/data 0.0081 data/processed/megatron_format/mmistral_websight_shard_007_pack32k/data 0.0081 data/processed/megatron_format/mmistral_websight_shard_008_pack32k/data 0.0081 data/processed/megatron_format/mmistral_websight_shard_009_pack32k/data 0.0081 data/processed/megatron_format/mmistral_websight_shard_010_pack32k/data 0.0081 data/processed/megatron_format/mmistral_websight_shard_011_pack32k/data 0.0081 data/processed/megatron_format/mmistral_websight_shard_012_pack32k/data 0.0080 data/processed/megatron_format/mmistral_websight_shard_013_pack32k/data 0.0081 data/processed/megatron_format/mmistral_websight_shard_014_pack32k/data 0.0081 data/processed/megatron_format/mmistral_websight_shard_015_pack32k/data 0.0081 data/processed/megatron_format/mmistral_websight_shard_016_pack32k/data 0.0080 data/processed/megatron_format/mmistral_websight_shard_017_pack32k/data 0.0080 data/processed/megatron_format/mmistral_websight_shard_018_pack32k/data 0.0076 data/processed/megatron_format/mmistral_websight_shard_019_pack32k/data 0.0036 data/processed/megatron_format/mmistral_ocrvqa_pack32k/data 0.0065 data/processed/megatron_format/mmistral_dvqa_pack32k/data 0.0034 data/processed/megatron_format/mmistral_figureqa_pack32k/data\n",
      "=========================\n"
     ]
    }
   ],
   "source": [
    "# Stage 2\n",
    "\n",
    "capfusion_subset_shard_ids_v7 = {f\"{i:03d}\" for i in range(32)}\n",
    "capfusion_subset_v7 = [f for f in capfusion[\"filepath\"].unique() if re.search(r\"shard_(\\d+)\", f).group(1) in capfusion_subset_shard_ids_v7]\n",
    "capfusion_subset_v7 = capfusion[capfusion[\"filepath\"].isin(capfusion_subset_v7)]\n",
    "\n",
    "train_mixture_v7_1_stage2_high_qual_caption_and_ocr = generate_data_prefix_with_natural_weights(\n",
    "    [\n",
    "\n",
    "        capfusion_subset_v7, # capfusion 32\n",
    "\n",
    "\n",
    "        detailed_caption,\n",
    "        cc3m_full,\n",
    "        slimpajama_shard1,\n",
    "        \n",
    "        llavaR,\n",
    "        websight,\n",
    "        ocrvqa,\n",
    "        dvqa,\n",
    "        figureqa,\n",
    "    ]\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "  background-color: #482173;\n",
       "  color: #f1f1f1;\n",
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       "  background-color: #3c4f8a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_efd30_row4_col3 {\n",
       "  background-color: #472e7c;\n",
       "  color: #f1f1f1;\n",
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       "#T_efd30_row4_col4 {\n",
       "  background-color: #3b528b;\n",
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       "#T_efd30_row4_col5 {\n",
       "  background-color: #238a8d;\n",
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       "#T_efd30_row5_col3 {\n",
       "  background-color: #2c718e;\n",
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       "<table id=\"T_efd30\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_efd30_level0_col0\" class=\"col_heading level0 col0\" >weight</th>\n",
       "      <th id=\"T_efd30_level0_col1\" class=\"col_heading level0 col1\" >n_instances</th>\n",
       "      <th id=\"T_efd30_level0_col2\" class=\"col_heading level0 col2\" >n_tokens</th>\n",
       "      <th id=\"T_efd30_level0_col3\" class=\"col_heading level0 col3\" >n_text_tokens</th>\n",
       "      <th id=\"T_efd30_level0_col4\" class=\"col_heading level0 col4\" >n_image_tokens</th>\n",
       "      <th id=\"T_efd30_level0_col5\" class=\"col_heading level0 col5\" >n_images</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >filepath</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_efd30_level0_row0\" class=\"row_heading level0 row0\" >data/processed/megatron_format/mmistral_slimpajama_shard_000_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_efd30_row0_col0\" class=\"data row0 col0\" >0.3153</td>\n",
       "      <td id=\"T_efd30_row0_col1\" class=\"data row0 col1\" >12,085</td>\n",
       "      <td id=\"T_efd30_row0_col2\" class=\"data row0 col2\" >430,688,442</td>\n",
       "      <td id=\"T_efd30_row0_col3\" class=\"data row0 col3\" >430,688,442</td>\n",
       "      <td id=\"T_efd30_row0_col4\" class=\"data row0 col4\" >0</td>\n",
       "      <td id=\"T_efd30_row0_col5\" class=\"data row0 col5\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_efd30_level0_row1\" class=\"row_heading level0 row1\" >data/processed/megatron_format/mmistral_llavaR_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_efd30_row1_col0\" class=\"data row1 col0\" >0.0940</td>\n",
       "      <td id=\"T_efd30_row1_col1\" class=\"data row1 col1\" >3,602</td>\n",
       "      <td id=\"T_efd30_row1_col2\" class=\"data row1 col2\" >117,448,784</td>\n",
       "      <td id=\"T_efd30_row1_col3\" class=\"data row1 col3\" >31,390,556</td>\n",
       "      <td id=\"T_efd30_row1_col4\" class=\"data row1 col4\" >86,058,228</td>\n",
       "      <td id=\"T_efd30_row1_col5\" class=\"data row1 col5\" >422,315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_efd30_level0_row2\" class=\"row_heading level0 row2\" >data/processed/megatron_format/mmistral_dvqa_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_efd30_row2_col0\" class=\"data row2 col0\" >0.0761</td>\n",
       "      <td id=\"T_efd30_row2_col1\" class=\"data row2 col1\" >2,917</td>\n",
       "      <td id=\"T_efd30_row2_col2\" class=\"data row2 col2\" >94,853,796</td>\n",
       "      <td id=\"T_efd30_row2_col3\" class=\"data row2 col3\" >55,653,796</td>\n",
       "      <td id=\"T_efd30_row2_col4\" class=\"data row2 col4\" >39,200,000</td>\n",
       "      <td id=\"T_efd30_row2_col5\" class=\"data row2 col5\" >200,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_efd30_level0_row3\" class=\"row_heading level0 row3\" >data/processed/megatron_format/mmistral_figureqa_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_efd30_row3_col0\" class=\"data row3 col0\" >0.0398</td>\n",
       "      <td id=\"T_efd30_row3_col1\" class=\"data row3 col1\" >1,526</td>\n",
       "      <td id=\"T_efd30_row3_col2\" class=\"data row3 col2\" >49,586,305</td>\n",
       "      <td id=\"T_efd30_row3_col3\" class=\"data row3 col3\" >24,803,256</td>\n",
       "      <td id=\"T_efd30_row3_col4\" class=\"data row3 col4\" >24,783,049</td>\n",
       "      <td id=\"T_efd30_row3_col5\" class=\"data row3 col5\" >100,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_efd30_level0_row4\" class=\"row_heading level0 row4\" >data/processed/megatron_format/mmistral_allava_vflan4v_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_efd30_row4_col0\" class=\"data row4 col0\" >0.1166</td>\n",
       "      <td id=\"T_efd30_row4_col1\" class=\"data row4 col1\" >4,469</td>\n",
       "      <td id=\"T_efd30_row4_col2\" class=\"data row4 col2\" >144,577,377</td>\n",
       "      <td id=\"T_efd30_row4_col3\" class=\"data row4 col3\" >77,835,919</td>\n",
       "      <td id=\"T_efd30_row4_col4\" class=\"data row4 col4\" >66,741,458</td>\n",
       "      <td id=\"T_efd30_row4_col5\" class=\"data row4 col5\" >207,549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_efd30_level0_row5\" class=\"row_heading level0 row5\" >data/processed/megatron_format/mmistral_allava_laion4v_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_efd30_row5_col0\" class=\"data row5 col0\" >0.3581</td>\n",
       "      <td id=\"T_efd30_row5_col1\" class=\"data row5 col1\" >13,725</td>\n",
       "      <td id=\"T_efd30_row5_col2\" class=\"data row5 col2\" >442,509,490</td>\n",
       "      <td id=\"T_efd30_row5_col3\" class=\"data row5 col3\" >176,660,898</td>\n",
       "      <td id=\"T_efd30_row5_col4\" class=\"data row5 col4\" >265,848,592</td>\n",
       "      <td id=\"T_efd30_row5_col5\" class=\"data row5 col5\" >438,992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9de2b736d0>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of files: 6\n",
      "Total text tokens: 797,032,867 (62.28%)\n",
      "Total image tokens: 482,631,327 (37.72%)\n",
      "Total tokens: 1,279,664,194\n",
      "Total instances: ** 38324 **\n"
     ]
    },
    {
     "data": {
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       "  background-color: #ece51b;\n",
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       "#T_0783a_row2_col1, #T_0783a_row2_col2, #T_0783a_row2_col3 {\n",
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       "  background-color: #482173;\n",
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       "#T_0783a_row5_col6 {\n",
       "  background-color: #3e4c8a;\n",
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       "</style>\n",
       "<table id=\"T_0783a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_0783a_level0_col0\" class=\"col_heading level0 col0\" >count</th>\n",
       "      <th id=\"T_0783a_level0_col1\" class=\"col_heading level0 col1\" >weight</th>\n",
       "      <th id=\"T_0783a_level0_col2\" class=\"col_heading level0 col2\" >n_instances</th>\n",
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       "      <th id=\"T_0783a_level0_col5\" class=\"col_heading level0 col5\" >n_image_tokens</th>\n",
       "      <th id=\"T_0783a_level0_col6\" class=\"col_heading level0 col6\" >n_images</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >filepath_prefix</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_0783a_level0_row0\" class=\"row_heading level0 row0\" >data/processed/megatron_format/mmistral_allava_laion4v_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_0783a_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "      <td id=\"T_0783a_row0_col1\" class=\"data row0 col1\" >0.3581</td>\n",
       "      <td id=\"T_0783a_row0_col2\" class=\"data row0 col2\" >13,725</td>\n",
       "      <td id=\"T_0783a_row0_col3\" class=\"data row0 col3\" >442,509,490</td>\n",
       "      <td id=\"T_0783a_row0_col4\" class=\"data row0 col4\" >176,660,898</td>\n",
       "      <td id=\"T_0783a_row0_col5\" class=\"data row0 col5\" >265,848,592</td>\n",
       "      <td id=\"T_0783a_row0_col6\" class=\"data row0 col6\" >438,992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0783a_level0_row1\" class=\"row_heading level0 row1\" >data/processed/megatron_format/mmistral_slimpajama_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_0783a_row1_col0\" class=\"data row1 col0\" >1</td>\n",
       "      <td id=\"T_0783a_row1_col1\" class=\"data row1 col1\" >0.3153</td>\n",
       "      <td id=\"T_0783a_row1_col2\" class=\"data row1 col2\" >12,085</td>\n",
       "      <td id=\"T_0783a_row1_col3\" class=\"data row1 col3\" >430,688,442</td>\n",
       "      <td id=\"T_0783a_row1_col4\" class=\"data row1 col4\" >430,688,442</td>\n",
       "      <td id=\"T_0783a_row1_col5\" class=\"data row1 col5\" >0</td>\n",
       "      <td id=\"T_0783a_row1_col6\" class=\"data row1 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0783a_level0_row2\" class=\"row_heading level0 row2\" >data/processed/megatron_format/mmistral_allava_vflan4v_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_0783a_row2_col0\" class=\"data row2 col0\" >1</td>\n",
       "      <td id=\"T_0783a_row2_col1\" class=\"data row2 col1\" >0.1166</td>\n",
       "      <td id=\"T_0783a_row2_col2\" class=\"data row2 col2\" >4,469</td>\n",
       "      <td id=\"T_0783a_row2_col3\" class=\"data row2 col3\" >144,577,377</td>\n",
       "      <td id=\"T_0783a_row2_col4\" class=\"data row2 col4\" >77,835,919</td>\n",
       "      <td id=\"T_0783a_row2_col5\" class=\"data row2 col5\" >66,741,458</td>\n",
       "      <td id=\"T_0783a_row2_col6\" class=\"data row2 col6\" >207,549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0783a_level0_row3\" class=\"row_heading level0 row3\" >data/processed/megatron_format/mmistral_llavaR_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_0783a_row3_col0\" class=\"data row3 col0\" >1</td>\n",
       "      <td id=\"T_0783a_row3_col1\" class=\"data row3 col1\" >0.0940</td>\n",
       "      <td id=\"T_0783a_row3_col2\" class=\"data row3 col2\" >3,602</td>\n",
       "      <td id=\"T_0783a_row3_col3\" class=\"data row3 col3\" >117,448,784</td>\n",
       "      <td id=\"T_0783a_row3_col4\" class=\"data row3 col4\" >31,390,556</td>\n",
       "      <td id=\"T_0783a_row3_col5\" class=\"data row3 col5\" >86,058,228</td>\n",
       "      <td id=\"T_0783a_row3_col6\" class=\"data row3 col6\" >422,315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0783a_level0_row4\" class=\"row_heading level0 row4\" >data/processed/megatron_format/mmistral_dvqa_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_0783a_row4_col0\" class=\"data row4 col0\" >1</td>\n",
       "      <td id=\"T_0783a_row4_col1\" class=\"data row4 col1\" >0.0761</td>\n",
       "      <td id=\"T_0783a_row4_col2\" class=\"data row4 col2\" >2,917</td>\n",
       "      <td id=\"T_0783a_row4_col3\" class=\"data row4 col3\" >94,853,796</td>\n",
       "      <td id=\"T_0783a_row4_col4\" class=\"data row4 col4\" >55,653,796</td>\n",
       "      <td id=\"T_0783a_row4_col5\" class=\"data row4 col5\" >39,200,000</td>\n",
       "      <td id=\"T_0783a_row4_col6\" class=\"data row4 col6\" >200,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0783a_level0_row5\" class=\"row_heading level0 row5\" >data/processed/megatron_format/mmistral_figureqa_pack32k/data.jsonl</th>\n",
       "      <td id=\"T_0783a_row5_col0\" class=\"data row5 col0\" >1</td>\n",
       "      <td id=\"T_0783a_row5_col1\" class=\"data row5 col1\" >0.0398</td>\n",
       "      <td id=\"T_0783a_row5_col2\" class=\"data row5 col2\" >1,526</td>\n",
       "      <td id=\"T_0783a_row5_col3\" class=\"data row5 col3\" >49,586,305</td>\n",
       "      <td id=\"T_0783a_row5_col4\" class=\"data row5 col4\" >24,803,256</td>\n",
       "      <td id=\"T_0783a_row5_col5\" class=\"data row5 col5\" >24,783,049</td>\n",
       "      <td id=\"T_0783a_row5_col6\" class=\"data row5 col6\" >100,000</td>\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===== FINAL MIXTURE =====\n",
      "0.3153 data/processed/megatron_format/mmistral_slimpajama_shard_000_pack32k/data 0.0940 data/processed/megatron_format/mmistral_llavaR_pack32k/data 0.0761 data/processed/megatron_format/mmistral_dvqa_pack32k/data 0.0398 data/processed/megatron_format/mmistral_figureqa_pack32k/data 0.1166 data/processed/megatron_format/mmistral_allava_vflan4v_pack32k/data 0.3581 data/processed/megatron_format/mmistral_allava_laion4v_pack32k/data\n",
      "=========================\n"
     ]
    }
   ],
   "source": [
    "# Stage 3\n",
    "\n",
    "train_mixture_v7_1_stage3 = generate_data_prefix_with_natural_weights(\n",
    "    [\n",
    "        slimpajama_shard000.sample(frac=0.1), # downsample\n",
    "        llavaR,\n",
    "        dvqa,\n",
    "        figureqa,\n",
    "        allava_vflan4v,\n",
    "        allava_laion4v,\n",
    "    ]\n",
    ")\n"
   ]
  }
 ],
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